This dataset is from Kaggle and contains information from the 2016 American Community Survey. It includes ethnographic, education and economic data, as well as data about people without internet by state and county. The dataset is robust, and provides additional demographic information such as median age, median income, and median rent. However, it does not have data from many counties. While this is problematic, the American Community Survey does exclude data if it is not statistically significant. Despite this, the dataset is still worth considering, since it comprises 820 counties of 3,000.
This is an interesting dataset to consider because people may often think internet accessibility to be ubiquitous, but for a myriad of reasons it is not. This data is obtained through the question “Does your household have a broadband internet subscription?” While this does not account for access through devices like cell phones and tablets with 4G for example, it does still demonstrate some potential concerns if a community has very high rates of households without internet access.
I would posit after considering the data that communities that have more people without internet will have lower educational attainment and higher rates of poverty. As the internet becomes less of a luxury, and more of a necessity, it will be imperative that these communities have better access to internet, to encourage higher quality education and access to helpful and potentially lifesaving information. It will first be important to learn what the average percentage of persons without internet is and determine the standard deviation. This will make it easy to understand which communities have exceptionally good access to internet, and which do not. Much like there are food deserts, are there “internet deserts” as well? If so, where are they? And is there a correlation to education or income?
#Since the warnings were not problematic, I suppressed them.
import warnings
warnings.filterwarnings('ignore')
#standard imports
import numpy as np
import pandas as pd
import os
#matplotlib
import matplotlib.pyplot as plt
%matplotlib inline
from matplotlib.pyplot import figure
#plotly
import plotly.plotly as py
from plotly.graph_objs import *
import plotly
import plotly.tools as tls
import plotly.graph_objs as go
import plotly.figure_factory as ff
from plotly import __version__
#plotly offline
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
print(__version__) # requires version >= 1.9.0
init_notebook_mode(connected=True)
#scikitlearn
from sklearn import datasets, linear_model
from sklearn.metrics import mean_squared_error, r2_score
import sklearn
sklearn.__version__
from sklearn import datasets, linear_model
# Scientific libraries
from numpy import arange,array,ones
from scipy import stats
3.1.0
Below, you will find the dataset that I used in this project. It contains 820 rows and 23 columns and lists the county and state, as well as education, median income for the county, population of people in the county below the poverty line, and percent of people without internet access in that county. The educational data is listed by the number of people with that education level. The ethnographic information is also listed by the number of persons that identified as that race in the county. You may notice the GEOID as well. This is an identifier used by the US Government to give a unique code to each county. I had to clean up the data to avoid type errors, and maintained the original dataset as df.
#Read in dataset without truncation
df = pd.read_csv('~/Desktop/Python Exercises/kaggle_internet.csv')
pd.set_option('display.max_rows', 820)
pd.set_option('display.max_columns', 23)
df
| county | state | GEOID | lon | lat | P_total | P_white | P_black | P_asian | P_native | P_hawaiian | P_others | P_below_middle_school | P_some_high_school | P_high_school_equivalent | P_some_college | P_bachelor_and_above | P_below_poverty | median_age | gini_index | median_household_income | median_rent_per_income | percent_no_internet | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Anchorage Municipality | AK | 05000US02020 | -149.274354 | 61.177549 | 298192 | 184841.0 | 16102.0 | 27142.0 | 23916.0 | 7669.0 | 7935.0 | 2234.0 | 8196.0 | 44804.0 | 66162.0 | 70713.0 | 18302 | 33.0 | 0.4018 | 85634 | 28.0 | 6.593887 |
| 1 | Fairbanks North Star Borough | AK | 05000US02090 | -146.599867 | 64.690832 | 100605 | 75501.0 | 4385.0 | 3875.0 | 7427.0 | 503.0 | 2357.0 | 924.0 | 1527.0 | 14725.0 | 24570.0 | 19257.0 | 9580 | 30.6 | 0.3756 | 77328 | 25.6 | 12.102458 |
| 2 | Matanuska-Susitna Borough | AK | 05000US02170 | -149.407974 | 62.182173 | 104365 | 86314.0 | 1019.0 | 1083.0 | 5455.0 | 141.0 | 325.0 | 337.0 | 2755.0 | 21071.0 | 28472.0 | 12841.0 | 9893 | 34.2 | 0.4351 | 69332 | 29.6 | 11.156575 |
| 3 | Baldwin County | AL | 05000US01003 | -87.746067 | 30.659218 | 208563 | 180484.0 | 18821.0 | 914.0 | 1383.0 | 0.0 | 1469.0 | 3245.0 | 10506.0 | 41822.0 | 46790.0 | 43547.0 | 23375 | 42.4 | 0.4498 | 56732 | 29.3 | 17.868167 |
| 4 | Calhoun County | AL | 05000US01015 | -85.822513 | 33.771706 | 114611 | NaN | NaN | NaN | NaN | NaN | NaN | 2455.0 | 8853.0 | 24761.0 | 26625.0 | 12909.0 | 18193 | 39.1 | 0.4692 | 41687 | 24.8 | 23.464932 |
| 5 | Cullman County | AL | 05000US01043 | -86.869267 | 34.131923 | 82471 | NaN | NaN | NaN | NaN | NaN | NaN | 3273.0 | 8398.0 | 18481.0 | 16268.0 | 9732.0 | 11524 | 40.4 | 0.4518 | 39411 | 29.7 | 23.294498 |
| 6 | DeKalb County | AL | 05000US01049 | -85.803992 | 34.460929 | 70900 | NaN | NaN | NaN | NaN | NaN | NaN | 2608.0 | 7356.0 | 15325.0 | 15319.0 | 5243.0 | 15029 | 39.8 | 0.4528 | 35963 | 31.4 | 28.720009 |
| 7 | Elmore County | AL | 05000US01051 | -86.142739 | 32.597229 | 81799 | NaN | NaN | NaN | NaN | NaN | NaN | 835.0 | 5215.0 | 17016.0 | 17118.0 | 14645.0 | 11283 | 38.3 | 0.4535 | 52579 | 33.6 | 13.805792 |
| 8 | Etowah County | AL | 05000US01055 | -86.034420 | 34.047638 | 102564 | NaN | NaN | NaN | NaN | NaN | NaN | 2880.0 | 8685.0 | 24731.0 | 22151.0 | 11075.0 | 16955 | 41.2 | 0.4477 | 41152 | 26.1 | 19.155961 |
| 9 | Houston County | AL | 05000US01069 | -85.296398 | 31.158193 | 104056 | 71838.0 | 27762.0 | 1054.0 | 443.0 | 0.0 | 1031.0 | 1698.0 | 8156.0 | 21501.0 | 24051.0 | 14517.0 | 20571 | 39.5 | 0.4799 | 42321 | 29.4 | 25.738504 |
| 10 | Jefferson County | AL | 05000US01073 | -86.896536 | 33.553444 | 659521 | 340506.0 | 279979.0 | 8366.0 | 1799.0 | 323.0 | 14127.0 | 7423.0 | 35046.0 | 117923.0 | 135554.0 | 147831.0 | 97105 | 38.0 | 0.5145 | 50180 | 29.9 | 18.017671 |
| 11 | Lauderdale County | AL | 05000US01077 | -87.650997 | 34.904122 | 92318 | NaN | NaN | NaN | NaN | NaN | NaN | 1678.0 | 4841.0 | 22912.0 | 17777.0 | 13815.0 | 12965 | 41.6 | 0.4424 | 43427 | 25.3 | 23.669967 |
| 12 | Lee County | AL | 05000US01081 | -85.353048 | 32.604064 | 158991 | NaN | NaN | NaN | NaN | NaN | NaN | 2683.0 | 7493.0 | 21014.0 | 28516.0 | 33905.0 | 29192 | 31.0 | 0.4773 | 48056 | 29.3 | 13.170132 |
| 13 | Limestone County | AL | 05000US01083 | -86.981399 | 34.810239 | 92753 | NaN | NaN | NaN | NaN | NaN | NaN | 2061.0 | 8374.0 | 19322.0 | 18124.0 | 14228.0 | 11995 | 38.8 | 0.4602 | 50872 | 27.9 | 16.643664 |
| 14 | Madison County | AL | 05000US01089 | -86.551080 | 34.764238 | 356967 | 243688.0 | 87415.0 | 8383.0 | 2547.0 | 250.0 | 4835.0 | 5829.0 | 14080.0 | 55090.0 | 66161.0 | 98936.0 | 49174 | 38.8 | 0.4803 | 60503 | 29.2 | 11.749229 |
| 15 | Marshall County | AL | 05000US01095 | -86.321668 | 34.309564 | 95157 | NaN | NaN | NaN | NaN | NaN | NaN | 3284.0 | 7053.0 | 21479.0 | 21168.0 | 10531.0 | 21870 | 38.0 | 0.4788 | 42362 | 29.4 | 19.308299 |
| 16 | Mobile County | AL | 05000US01097 | -88.196568 | 30.684573 | 414836 | 243284.0 | 148001.0 | 8269.0 | 3328.0 | 20.0 | 4702.0 | 4425.0 | 29557.0 | 89541.0 | 85667.0 | 65919.0 | 78468 | 37.4 | 0.4756 | 45744 | 31.1 | 22.561903 |
| 17 | Montgomery County | AL | 05000US01101 | -86.203831 | 32.203651 | 226349 | 81740.0 | 130006.0 | 5753.0 | 339.0 | 36.0 | 3774.0 | 3958.0 | 15674.0 | 36639.0 | 44409.0 | 48055.0 | 39278 | 36.4 | 0.5013 | 45395 | 32.4 | 18.443855 |
| 18 | Morgan County | AL | 05000US01103 | -86.846402 | 34.454484 | 119012 | 93591.0 | 14431.0 | 1076.0 | 763.0 | 0.0 | 6454.0 | 3238.0 | 9033.0 | 24468.0 | 25545.0 | 16219.0 | 19679 | 40.7 | 0.4814 | 44378 | 23.2 | 24.689843 |
| 19 | St. Clair County | AL | 05000US01115 | -86.315663 | 33.712963 | 88019 | NaN | NaN | NaN | NaN | NaN | NaN | 1352.0 | 7856.0 | 22149.0 | 17659.0 | 10665.0 | 8971 | 40.3 | 0.3772 | 60158 | 28.0 | 17.188044 |
| 20 | Shelby County | AL | 05000US01117 | -86.678104 | 33.262937 | 210622 | 168484.0 | 24066.0 | 4535.0 | 211.0 | 197.0 | 8566.0 | 1709.0 | 7885.0 | 27924.0 | 43895.0 | 60429.0 | 16706 | 38.9 | 0.4305 | 73647 | 24.3 | 8.630142 |
| 21 | Talladega County | AL | 05000US01121 | -86.175804 | 33.369277 | 80103 | NaN | NaN | NaN | NaN | NaN | NaN | 1484.0 | 7572.0 | 18221.0 | 19081.0 | 8402.0 | 12706 | 40.8 | 0.4510 | 39393 | 28.2 | 22.032554 |
| 22 | Tuscaloosa County | AL | 05000US01125 | -87.522860 | 33.290202 | 206102 | NaN | NaN | NaN | NaN | NaN | NaN | 2513.0 | 10441.0 | 35820.0 | 39734.0 | 38466.0 | 34792 | 32.9 | 0.4748 | 47787 | 32.9 | 19.624788 |
| 23 | Walker County | AL | 05000US01127 | -87.301092 | 33.791571 | 64967 | NaN | NaN | NaN | NaN | NaN | NaN | 1964.0 | 5733.0 | 17432.0 | 13423.0 | 5016.0 | 13919 | 41.8 | 0.4380 | 39068 | 31.8 | 23.794158 |
| 24 | Benton County | AR | 05000US05007 | -94.256187 | 36.337825 | 258291 | 228649.0 | 3896.0 | 9343.0 | 2656.0 | 1350.0 | 3509.0 | 7268.0 | 9534.0 | 49236.0 | 45237.0 | 54165.0 | 22874 | 35.4 | 0.4329 | 63631 | 22.2 | 14.930048 |
| 25 | Craighead County | AR | 05000US05031 | -90.630411 | 35.828268 | 105835 | NaN | NaN | NaN | NaN | NaN | NaN | 1942.0 | 4798.0 | 25343.0 | 18204.0 | 15874.0 | 16602 | 34.5 | 0.5027 | 43678 | 27.0 | 16.348931 |
| 26 | Faulkner County | AR | 05000US05045 | -92.324654 | 35.146356 | 122227 | 100295.0 | 12771.0 | 1404.0 | 277.0 | 739.0 | 2809.0 | 900.0 | 4581.0 | 23926.0 | 22803.0 | 21557.0 | 22285 | 33.1 | 0.4724 | 48506 | 29.3 | 15.878901 |
| 27 | Garland County | AR | 05000US05051 | -93.146915 | 34.578861 | 97477 | NaN | NaN | NaN | NaN | NaN | NaN | 913.0 | 6475.0 | 20535.0 | 27843.0 | 12701.0 | 17824 | 44.0 | 0.4441 | 42826 | 30.9 | 17.736396 |
| 28 | Jefferson County | AR | 05000US05069 | -91.930701 | 34.277696 | 70016 | NaN | NaN | NaN | NaN | NaN | NaN | 1913.0 | 5724.0 | 16087.0 | 13032.0 | 8694.0 | 14565 | 39.0 | 0.4748 | 37712 | 28.6 | 31.988785 |
| 29 | Lonoke County | AR | 05000US05085 | -91.894132 | 34.755114 | 72228 | 63910.0 | 3884.0 | 1032.0 | 292.0 | 0.0 | 1309.0 | 548.0 | 3251.0 | 16853.0 | 16004.0 | 10186.0 | 10548 | 36.4 | 0.3900 | 55837 | 26.2 | 19.633836 |
| 30 | Pulaski County | AR | 05000US05119 | -92.316515 | 34.773988 | 393250 | 218346.0 | 142430.0 | 8604.0 | 799.0 | 33.0 | 9119.0 | 4258.0 | 15988.0 | 68957.0 | 82502.0 | 90368.0 | 68881 | 36.8 | 0.5132 | 47387 | 29.1 | 19.187059 |
| 31 | Saline County | AR | 05000US05125 | -92.674463 | 34.648525 | 118703 | NaN | NaN | NaN | NaN | NaN | NaN | 838.0 | 5217.0 | 26651.0 | 25394.0 | 23248.0 | 8802 | 41.0 | 0.3820 | 64932 | 24.2 | 12.353877 |
| 32 | Sebastian County | AR | 05000US05131 | -94.274989 | 35.196981 | 127793 | 93101.0 | 8645.0 | 5547.0 | 1893.0 | 74.0 | 13978.0 | 4867.0 | 7636.0 | 29228.0 | 28434.0 | 14897.0 | 21913 | 37.8 | 0.4583 | 42053 | 26.9 | 19.473117 |
| 33 | Washington County | AR | 05000US05143 | -94.218417 | 35.971209 | 228049 | 174457.0 | 8608.0 | 5402.0 | 2189.0 | 4953.0 | 25446.0 | 7095.0 | 9994.0 | 35733.0 | 36425.0 | 44638.0 | 36641 | 31.5 | 0.4917 | 45679 | 28.5 | 15.143759 |
| 34 | White County | AR | 05000US05145 | -91.753158 | 35.254722 | 79263 | NaN | NaN | NaN | NaN | NaN | NaN | 626.0 | 4532.0 | 20224.0 | 13896.0 | 11030.0 | 10941 | 35.8 | 0.4236 | 42844 | 30.7 | 23.751816 |
| 35 | Apache County | AZ | 05000US04001 | -109.493747 | 35.385845 | 73112 | 16162.0 | 112.0 | 106.0 | 53520.0 | 54.0 | 694.0 | 3238.0 | 4230.0 | 14071.0 | 17542.0 | 5263.0 | 24122 | 34.6 | 0.4892 | 34685 | 17.3 | 54.011390 |
| 36 | Cochise County | AZ | 05000US04003 | -109.754120 | 31.881793 | 125770 | 109899.0 | 5077.0 | 2783.0 | 1875.0 | 434.0 | 1913.0 | 3800.0 | 6377.0 | 22897.0 | 31858.0 | 21340.0 | 26604 | 40.8 | 0.4325 | 45508 | 27.1 | 17.925300 |
| 37 | Coconino County | AZ | 05000US04005 | -111.773728 | 35.829692 | 140908 | 91167.0 | 2166.0 | 2670.0 | 37010.0 | 163.0 | 2711.0 | 1896.0 | 5541.0 | 18074.0 | 26263.0 | 28973.0 | 22902 | 30.9 | 0.4525 | 55091 | 30.4 | 18.389301 |
| 38 | Maricopa County | AZ | 05000US04013 | -112.495533 | 33.346541 | 4242997 | 3227510.0 | 230642.0 | 168720.0 | 82300.0 | 9808.0 | 371556.0 | 123066.0 | 193976.0 | 629861.0 | 922027.0 | 896358.0 | 626082 | 36.2 | 0.4699 | 58737 | 29.0 | 12.044591 |
| 39 | Mohave County | AZ | 05000US04015 | -113.749689 | 35.717705 | 205249 | 187492.0 | 1614.0 | 2029.0 | 4870.0 | 231.0 | 3539.0 | 3077.0 | 18637.0 | 53416.0 | 60615.0 | 17523.0 | 35646 | 50.6 | 0.4645 | 42423 | 28.2 | 16.624252 |
| 40 | Navajo County | AZ | 05000US04017 | -110.320908 | 35.390934 | 110026 | 50543.0 | 755.0 | 897.0 | 49003.0 | 0.0 | 4536.0 | 2938.0 | 8116.0 | 21081.0 | 26042.0 | 10173.0 | 30844 | 36.3 | 0.4847 | 36998 | 27.8 | 36.002025 |
| 41 | Pima County | AZ | 05000US04019 | -111.783018 | 32.128237 | 1016206 | 774608.0 | 34344.0 | 28713.0 | 32670.0 | 1550.0 | 94614.0 | 21541.0 | 48684.0 | 149691.0 | 230298.0 | 215628.0 | 181277 | 38.3 | 0.4684 | 47560 | 30.3 | 12.641611 |
| 42 | Pinal County | AZ | 05000US04021 | -111.367257 | 32.918910 | 418540 | 337571.0 | 18679.0 | 8568.0 | 21796.0 | 1357.0 | 17937.0 | 8960.0 | 26252.0 | 80736.0 | 106961.0 | 58235.0 | 61143 | 39.2 | 0.4407 | 52555 | 29.2 | 13.110493 |
| 43 | Yavapai County | AZ | 05000US04025 | -112.573745 | 34.630044 | 225562 | 209960.0 | 1130.0 | 2368.0 | 3491.0 | 207.0 | 4025.0 | 1506.0 | 9954.0 | 47109.0 | 70385.0 | 41400.0 | 28082 | 53.1 | 0.4627 | 50420 | 30.4 | 17.079463 |
| 44 | Yuma County | AZ | 05000US04027 | -113.910905 | 32.773942 | 205631 | 112651.0 | 3696.0 | 2719.0 | 2926.0 | 266.0 | 78192.0 | 14696.0 | 14229.0 | 34499.0 | 41773.0 | 20023.0 | 36136 | 34.8 | 0.4269 | 43518 | 27.0 | 18.006897 |
| 45 | Alameda County | CA | 05000US06001 | -121.913304 | 37.648081 | 1647704 | 688067.0 | 174855.0 | 483856.0 | 10122.0 | 13865.0 | 171152.0 | 63504.0 | 63957.0 | 203437.0 | 287522.0 | 526093.0 | 172923 | 37.4 | 0.4604 | 89979 | 29.8 | 10.138773 |
| 46 | Butte County | CA | 05000US06007 | -121.601919 | 39.665959 | 226864 | 187892.0 | 3563.0 | 10665.0 | 2567.0 | 811.0 | 9577.0 | 5192.0 | 10017.0 | 34807.0 | 58155.0 | 38076.0 | 43838 | 38.1 | 0.4888 | 45177 | 38.5 | 13.589225 |
| 47 | Contra Costa County | CA | 05000US06013 | -121.951543 | 37.919479 | 1135127 | 658852.0 | 97413.0 | 183849.0 | 3159.0 | 4891.0 | 104400.0 | 37361.0 | 35488.0 | 133598.0 | 239017.0 | 323443.0 | 97049 | 39.5 | 0.4596 | 91045 | 31.1 | 7.169144 |
| 48 | El Dorado County | CA | 05000US06017 | -120.534398 | 38.785532 | 185625 | 162495.0 | 1795.0 | 8904.0 | 2379.0 | 462.0 | 3745.0 | 3030.0 | 5222.0 | 29859.0 | 49951.0 | 45596.0 | 15373 | 46.1 | 0.4609 | 75772 | 29.2 | 10.588202 |
| 49 | Fresno County | CA | 05000US06019 | -119.655019 | 36.761006 | 979915 | 640619.0 | 47646.0 | 101993.0 | 10176.0 | 1183.0 | 143649.0 | 70135.0 | 64384.0 | 139362.0 | 187031.0 | 120826.0 | 247507 | 32.1 | 0.4910 | 48715 | 36.5 | 17.471044 |
| 50 | Humboldt County | CA | 05000US06023 | -123.925818 | 40.706673 | 136646 | 110795.0 | 1172.0 | 4212.0 | 7228.0 | 0.0 | 3154.0 | 991.0 | 6924.0 | 23705.0 | 32405.0 | 27020.0 | 26945 | 37.9 | 0.5038 | 43130 | 35.3 | 14.925318 |
| 51 | Imperial County | CA | 05000US06025 | -115.355395 | 33.040816 | 180883 | 110971.0 | 4122.0 | 2731.0 | 1808.0 | 301.0 | 54515.0 | 13792.0 | 16748.0 | 26038.0 | 34525.0 | 15162.0 | 42303 | 32.5 | 0.4746 | 49095 | 31.8 | 22.574964 |
| 52 | Kern County | CA | 05000US06029 | -118.729506 | 35.346629 | 884788 | 642077.0 | 47668.0 | 40615.0 | 11936.0 | 3142.0 | 106937.0 | 59354.0 | 68973.0 | 144166.0 | 162350.0 | 86885.0 | 193133 | 31.3 | 0.4644 | 49903 | 32.7 | 17.331400 |
| 53 | Kings County | CA | 05000US06031 | -119.815530 | 36.072478 | 149785 | 99706.0 | 10028.0 | 5238.0 | 2083.0 | 472.0 | 25665.0 | 8953.0 | 12668.0 | 28265.0 | 29719.0 | 10671.0 | 21565 | 31.8 | 0.4321 | 53234 | 28.4 | 17.296356 |
| 54 | Lake County | CA | 05000US06033 | -122.746757 | 39.094802 | 64116 | NaN | NaN | NaN | NaN | NaN | NaN | 1948.0 | 4675.0 | 11805.0 | 20786.0 | 7617.0 | 12973 | 46.5 | 0.4763 | 42029 | 34.2 | 23.104135 |
| 55 | Los Angeles County | CA | 05000US06037 | -118.261862 | 34.196398 | 10137915 | 5093898.0 | 826516.0 | 1474575.0 | 65370.0 | 26104.0 | 2258451.0 | 744735.0 | 604335.0 | 1433558.0 | 1787322.0 | 2167313.0 | 1628305 | 36.3 | 0.5030 | 61338 | 34.5 | 14.209510 |
| 56 | Madera County | CA | 05000US06039 | -119.749852 | 37.210039 | 154697 | 109705.0 | 4535.0 | 2748.0 | 3262.0 | 312.0 | 28121.0 | 13445.0 | 11722.0 | 24199.0 | 32648.0 | 11339.0 | 29736 | 33.6 | 0.4481 | 51657 | 33.7 | 20.297956 |
| 57 | Marin County | CA | 05000US06041 | -122.745974 | 38.051817 | 260651 | 200426.0 | 6114.0 | 13886.0 | 274.0 | 397.0 | 25607.0 | 8411.0 | 3776.0 | 19769.0 | 44814.0 | 112386.0 | 19762 | 46.1 | 0.5245 | 103845 | 30.5 | 7.208487 |
| 58 | Mendocino County | CA | 05000US06045 | -123.442881 | 39.432388 | 87628 | 71202.0 | 412.0 | 1125.0 | 3169.0 | 231.0 | 5887.0 | 3776.0 | 5269.0 | 15184.0 | 22807.0 | 13734.0 | 16881 | 42.8 | 0.5156 | 43809 | 37.4 | 16.275854 |
| 59 | Merced County | CA | 05000US06047 | -120.722802 | 37.194806 | 268672 | 149377.0 | 8492.0 | 21156.0 | 2522.0 | 32.0 | 77857.0 | 26343.0 | 20093.0 | 38469.0 | 47303.0 | 21342.0 | 53314 | 30.9 | 0.4969 | 47739 | 29.7 | 15.371945 |
| 60 | Monterey County | CA | 05000US06053 | -121.315573 | 36.240107 | 435232 | 242774.0 | 11055.0 | 24852.0 | 1903.0 | 1086.0 | 135093.0 | 45225.0 | 24535.0 | 52379.0 | 72620.0 | 68343.0 | 52257 | 34.0 | 0.4514 | 63876 | 32.0 | 17.628440 |
| 61 | Napa County | CA | 05000US06055 | -122.325995 | 38.507351 | 142166 | 110943.0 | 2718.0 | 11301.0 | 1160.0 | 269.0 | 10439.0 | 7457.0 | 5699.0 | 17316.0 | 32157.0 | 34822.0 | 10032 | 41.0 | 0.4641 | 75077 | 29.9 | 11.858114 |
| 62 | Nevada County | CA | 05000US06057 | -120.773446 | 39.295191 | 99107 | 91701.0 | 197.0 | 1742.0 | 490.0 | 0.0 | 1414.0 | 773.0 | 4665.0 | 12973.0 | 29321.0 | 27690.0 | 10505 | 50.2 | 0.4784 | 59022 | 34.3 | 11.390410 |
| 63 | Orange County | CA | 05000US06059 | -117.777207 | 33.675687 | 3172532 | 1986194.0 | 53602.0 | 637622.0 | 13443.0 | 10791.0 | 352857.0 | 148793.0 | 145823.0 | 369136.0 | 600961.0 | 862800.0 | 346002 | 37.7 | 0.4695 | 81837 | 33.1 | 7.381774 |
| 64 | Placer County | CA | 05000US06061 | -120.722718 | 39.062032 | 380531 | 309901.0 | 5796.0 | 26369.0 | 2292.0 | 392.0 | 15303.0 | 4001.0 | 7848.0 | 46015.0 | 103240.0 | 104141.0 | 28064 | 41.9 | 0.4571 | 85426 | 30.0 | 8.078847 |
| 65 | Riverside County | CA | 05000US06065 | -116.002239 | 33.729827 | 2387741 | 1406044.0 | 152433.0 | 149105.0 | 20442.0 | 7583.0 | 544724.0 | 120739.0 | 145309.0 | 402375.0 | 506515.0 | 335035.0 | 360386 | 35.3 | 0.4559 | 60134 | 34.2 | 12.419725 |
| 66 | Sacramento County | CA | 05000US06067 | -121.340441 | 38.450011 | 1514460 | 882763.0 | 148706.0 | 230645.0 | 10265.0 | 16214.0 | 117768.0 | 56814.0 | 66821.0 | 230687.0 | 345423.0 | 302855.0 | 245111 | 36.0 | 0.4649 | 59780 | 32.5 | 11.045058 |
| 67 | San Bernardino County | CA | 05000US06071 | -116.181197 | 34.857220 | 2140096 | 1314742.0 | 182864.0 | 152270.0 | 15077.0 | 6381.0 | 364338.0 | 101683.0 | 157577.0 | 351679.0 | 433564.0 | 267503.0 | 368528 | 33.2 | 0.4400 | 56337 | 34.3 | 12.132071 |
| 68 | San Diego County | CA | 05000US06073 | -116.776117 | 33.023604 | 3317749 | 2385470.0 | 165878.0 | 389369.0 | 24980.0 | 14356.0 | 169165.0 | 126358.0 | 148766.0 | 411111.0 | 684874.0 | 835451.0 | 398475 | 35.7 | 0.4644 | 70824 | 33.7 | 9.549978 |
| 69 | San Francisco County | CA | 05000US06075 | -123.032229 | 37.727239 | 870887 | 404410.0 | 43468.0 | 300409.0 | 3054.0 | 1967.0 | 70599.0 | 40123.0 | 32703.0 | 84652.0 | 126771.0 | 394813.0 | 86900 | 38.0 | 0.5029 | 103801 | 24.6 | 10.034485 |
| 70 | San Joaquin County | CA | 05000US06077 | -121.272237 | 37.935034 | 733709 | 417952.0 | 52126.0 | 111547.0 | 5428.0 | 4697.0 | 74430.0 | 43819.0 | 51513.0 | 126697.0 | 151553.0 | 77034.0 | 103399 | 34.3 | 0.4466 | 59518 | 31.6 | 16.190698 |
| 71 | San Luis Obispo County | CA | 05000US06079 | -120.447540 | 35.385227 | 282887 | 238692.0 | 5167.0 | 10989.0 | 2262.0 | 28.0 | 16126.0 | 5377.0 | 8615.0 | 35036.0 | 70091.0 | 68286.0 | 28364 | 38.8 | 0.4376 | 70564 | 29.2 | 10.878358 |
| 72 | San Mateo County | CA | 05000US06081 | -122.371542 | 37.414664 | 764797 | 388724.0 | 17413.0 | 214801.0 | 3102.0 | 10756.0 | 85183.0 | 29943.0 | 24778.0 | 84004.0 | 128261.0 | 275493.0 | 49373 | 39.5 | 0.4821 | 108627 | 29.5 | 7.291085 |
| 73 | Santa Barbara County | CA | 05000US06083 | -120.038485 | 34.537378 | 446170 | 341741.0 | 8202.0 | 24802.0 | 4863.0 | 94.0 | 46374.0 | 32195.0 | 17569.0 | 47780.0 | 84230.0 | 89313.0 | 59709 | 33.8 | 0.4707 | 67436 | 34.5 | 10.924612 |
| 74 | Santa Clara County | CA | 05000US06085 | -121.690622 | 37.220777 | 1919402 | 844820.0 | 47142.0 | 683532.0 | 8787.0 | 8602.0 | 227352.0 | 79892.0 | 68559.0 | 198127.0 | 280826.0 | 673605.0 | 177431 | 37.0 | 0.4645 | 111069 | 29.3 | 6.782741 |
| 75 | Santa Cruz County | CA | 05000US06087 | -122.007205 | 37.012488 | 274673 | 203973.0 | 2908.0 | 13692.0 | 1797.0 | 43.0 | 40674.0 | 13839.0 | 6871.0 | 28049.0 | 55684.0 | 72551.0 | 36169 | 37.4 | 0.4857 | 77613 | 35.3 | 9.528658 |
| 76 | Shasta County | CA | 05000US06089 | -122.043550 | 40.760522 | 179631 | 158293.0 | 1791.0 | 6016.0 | 2942.0 | 77.0 | 1522.0 | 1943.0 | 7170.0 | 31960.0 | 56179.0 | 27576.0 | 30786 | 42.1 | 0.4655 | 46724 | 34.7 | 17.224495 |
| 77 | Solano County | CA | 05000US06095 | -121.939594 | 38.267226 | 440207 | 219084.0 | 64438.0 | 66560.0 | 1568.0 | 3713.0 | 46315.0 | 13663.0 | 20668.0 | 70351.0 | 117183.0 | 76316.0 | 49802 | 37.9 | 0.4340 | 73900 | 30.7 | 8.367522 |
| 78 | Sonoma County | CA | 05000US06097 | -122.945194 | 38.532574 | 503070 | 371195.0 | 8632.0 | 21487.0 | 4397.0 | 1542.0 | 71200.0 | 19314.0 | 21317.0 | 66982.0 | 127657.0 | 122082.0 | 45561 | 42.1 | 0.4481 | 73929 | 32.9 | 9.903255 |
| 79 | Stanislaus County | CA | 05000US06099 | -121.002656 | 37.562384 | 541560 | 395715.0 | 17096.0 | 30615.0 | 3427.0 | 3166.0 | 68689.0 | 35558.0 | 36338.0 | 94190.0 | 111719.0 | 55620.0 | 76191 | 34.0 | 0.4532 | 54305 | 34.4 | 15.160139 |
| 80 | Sutter County | CA | 05000US06101 | -121.702758 | 39.035257 | 96651 | 69300.0 | 1672.0 | 14849.0 | 653.0 | 501.0 | 3331.0 | 6649.0 | 6252.0 | 13546.0 | 23334.0 | 9987.0 | 17985 | 35.9 | 0.4662 | 51397 | 31.3 | 16.537332 |
| 81 | Tulare County | CA | 05000US06107 | -118.780542 | 36.230453 | 460437 | 340284.0 | 6167.0 | 20506.0 | 6233.0 | 908.0 | 72610.0 | 37578.0 | 35771.0 | 69771.0 | 80388.0 | 36919.0 | 114290 | 30.8 | 0.4554 | 45881 | 32.1 | 18.494191 |
| 82 | Ventura County | CA | 05000US06111 | -119.133143 | 34.358742 | 849738 | 681115.0 | 14929.0 | 60772.0 | 5746.0 | 1787.0 | 45523.0 | 48163.0 | 35936.0 | 105220.0 | 181595.0 | 190429.0 | 79392 | 37.7 | 0.4474 | 80135 | 34.0 | 12.131702 |
| 83 | Yolo County | CA | 05000US06113 | -121.903178 | 38.679268 | 215802 | 151935.0 | 5476.0 | 30155.0 | 1835.0 | 836.0 | 11635.0 | 8756.0 | 9417.0 | 22892.0 | 33335.0 | 52163.0 | 41907 | 30.9 | 0.5057 | 64904 | 31.7 | 9.594141 |
| 84 | Yuba County | CA | 05000US06115 | -121.344280 | 39.270026 | 75275 | 56042.0 | 2331.0 | 3656.0 | 1046.0 | 1376.0 | 3971.0 | 1692.0 | 4688.0 | 10799.0 | 20110.0 | 8735.0 | 10921 | 32.4 | 0.4444 | 49259 | 32.4 | 15.172853 |
| 85 | Adams County | CO | 05000US08001 | -104.331872 | 39.874325 | 498187 | 402970.0 | 15865.0 | 19783.0 | 7677.0 | 425.0 | 32370.0 | 19922.0 | 29749.0 | 92677.0 | 97658.0 | 73008.0 | 57822 | 33.7 | 0.4006 | 66033 | 32.2 | 13.087137 |
| 86 | Arapahoe County | CO | 05000US08005 | -104.331733 | 39.644632 | 637068 | 452406.0 | 69336.0 | 38563.0 | 3623.0 | 1618.0 | 47841.0 | 9568.0 | 19604.0 | 94113.0 | 125857.0 | 178155.0 | 56327 | 36.5 | 0.4538 | 70950 | 31.0 | 8.686033 |
| 87 | Boulder County | CO | 05000US08013 | -105.398382 | 40.094826 | 322226 | 288623.0 | 3453.0 | 15560.0 | 927.0 | 209.0 | 5652.0 | 3054.0 | 6392.0 | 27928.0 | 44746.0 | 127679.0 | 34157 | 36.4 | 0.4840 | 74615 | 34.7 | 6.382652 |
| 88 | Broomfield County | CO | 05000US08014 | -105.052125 | 39.953383 | 66529 | 57480.0 | 727.0 | 3943.0 | 316.0 | 0.0 | 1812.0 | NaN | NaN | NaN | NaN | NaN | 5643 | 38.4 | 0.4614 | 84349 | 27.4 | 4.408238 |
| 89 | Denver County | CO | 05000US08031 | -104.880625 | 39.761849 | 693060 | 522284.0 | 68262.0 | 25721.0 | 5462.0 | 410.0 | 47322.0 | 20305.0 | 34126.0 | 89883.0 | 106830.0 | 234447.0 | 93188 | 34.4 | 0.5008 | 61105 | 30.4 | 11.419746 |
| 90 | Douglas County | CO | 05000US08035 | -104.926199 | 39.326435 | 328632 | 294600.0 | 4667.0 | 15524.0 | 1294.0 | 205.0 | 3739.0 | 1081.0 | 1920.0 | 24611.0 | 59646.0 | 128220.0 | 12037 | 38.9 | 0.4071 | 109292 | 29.1 | 2.660562 |
| 91 | El Paso County | CO | 05000US08041 | -104.527472 | 38.827383 | 688284 | 552441.0 | 43539.0 | 18301.0 | 3588.0 | 3009.0 | 25506.0 | 5902.0 | 16118.0 | 87447.0 | 158713.0 | 170871.0 | 76464 | 33.9 | 0.4321 | 63882 | 29.9 | 6.932988 |
| 92 | Jefferson County | CO | 05000US08059 | -105.245600 | 39.586459 | 571837 | 519734.0 | 7314.0 | 17174.0 | 3596.0 | 273.0 | 9742.0 | 4368.0 | 16307.0 | 81575.0 | 125690.0 | 178031.0 | 37593 | 40.2 | 0.4323 | 74186 | 32.1 | 6.587986 |
| 93 | Larimer County | CO | 05000US08069 | -105.482131 | 40.663091 | 339993 | 307362.0 | 3351.0 | 7489.0 | 2714.0 | 516.0 | 6735.0 | 1364.0 | 5554.0 | 46045.0 | 63903.0 | 103892.0 | 37699 | 35.9 | 0.4400 | 66469 | 34.2 | 7.874771 |
| 94 | Mesa County | CO | 05000US08077 | -108.461837 | 39.019492 | 150083 | 140112.0 | 465.0 | 1263.0 | 1384.0 | 467.0 | 2111.0 | 2159.0 | 8867.0 | 30598.0 | 33431.0 | 25198.0 | 23494 | 39.8 | 0.4346 | 48846 | 33.6 | 10.453911 |
| 95 | Pueblo County | CO | 05000US08101 | -104.489893 | 38.170658 | 165123 | 133330.0 | 4192.0 | 1310.0 | 8264.0 | 127.0 | 13092.0 | 1670.0 | 10044.0 | 31543.0 | 43130.0 | 23759.0 | 32757 | 39.1 | 0.4477 | 44677 | 32.8 | 18.726616 |
| 96 | Weld County | CO | 05000US08123 | -104.383649 | 40.555794 | 294932 | 271892.0 | 3435.0 | 4523.0 | 970.0 | 0.0 | 6937.0 | 6670.0 | 14259.0 | 54291.0 | 58987.0 | 51217.0 | 34393 | 34.3 | 0.4375 | 63400 | 31.5 | 11.473547 |
| 97 | Fairfield County | CT | 05000US09001 | -73.366757 | 41.228103 | 944177 | 696692.0 | 110693.0 | 50381.0 | 2811.0 | 695.0 | 52428.0 | 23085.0 | 29891.0 | 141427.0 | 136932.0 | 297908.0 | 79372 | 40.4 | 0.5404 | 90123 | 32.7 | 10.109508 |
| 98 | Hartford County | CT | 05000US09003 | -72.732916 | 41.806053 | 892389 | 636425.0 | 120762.0 | 45365.0 | 2763.0 | 82.0 | 57288.0 | 17405.0 | 37350.0 | 173731.0 | 149818.0 | 230416.0 | 94580 | 40.6 | 0.4695 | 69433 | 30.0 | 14.885449 |
| 99 | Litchfield County | CT | 05000US09005 | -73.235428 | 41.791897 | 182571 | NaN | NaN | NaN | NaN | NaN | NaN | 1661.0 | 5935.0 | 39025.0 | 37076.0 | 49581.0 | 12350 | 46.8 | 0.4497 | 76993 | 29.7 | 9.964240 |
| 100 | Middlesex County | CT | 05000US09007 | -72.524227 | 41.434525 | 163329 | 145795.0 | 8763.0 | 4893.0 | 5.0 | 0.0 | 791.0 | 1806.0 | 3504.0 | 32210.0 | 30049.0 | 49292.0 | 14077 | 45.6 | 0.4671 | 79739 | 28.6 | 12.848096 |
| 101 | New Haven County | CT | 05000US09009 | -72.900204 | 41.349717 | 856875 | 635115.0 | 110901.0 | 35579.0 | 1827.0 | 423.0 | 45612.0 | 15228.0 | 36959.0 | 181219.0 | 142740.0 | 208217.0 | 92957 | 39.9 | 0.4617 | 66176 | 30.9 | 13.932715 |
| 102 | New London County | CT | 05000US09011 | -72.103452 | 41.478630 | 269801 | 218961.0 | 15524.0 | 12084.0 | 1730.0 | 59.0 | 9528.0 | 1828.0 | 9560.0 | 57606.0 | 55381.0 | 60961.0 | 23140 | 41.5 | 0.4591 | 70699 | 27.9 | 11.652222 |
| 103 | Tolland County | CT | 05000US09013 | -72.340977 | 41.858076 | 151118 | NaN | NaN | NaN | NaN | NaN | NaN | 535.0 | 3382.0 | 26120.0 | 25910.0 | 37892.0 | 9052 | 37.7 | 0.4237 | 81252 | 32.8 | 11.576163 |
| 104 | Windham County | CT | 05000US09015 | -71.990702 | 41.824999 | 116192 | 103169.0 | 3606.0 | 1806.0 | 555.0 | 0.0 | 2263.0 | 1479.0 | 6841.0 | 27438.0 | 23532.0 | 19321.0 | 13600 | 40.8 | 0.4283 | 61608 | 27.9 | 15.809004 |
| 105 | District of Columbia | DC | 05000US11001 | -77.017094 | 38.904148 | 681170 | 277268.0 | 320554.0 | 26436.0 | 2004.0 | 41.0 | 37132.0 | 12245.0 | 28646.0 | 85248.0 | 76292.0 | 272875.0 | 120308 | 33.9 | 0.5420 | 75506 | 29.3 | 17.649631 |
| 106 | Kent County | DE | 05000US10001 | -75.502982 | 39.097088 | 174827 | 115964.0 | 43984.0 | 3125.0 | 961.0 | 9.0 | 2543.0 | 2624.0 | 9704.0 | 41865.0 | 33408.0 | 26391.0 | 23384 | 37.5 | 0.4152 | 54140 | 30.6 | 13.386706 |
| 107 | New Castle County | DE | 05000US10003 | -75.644132 | 39.575915 | 556987 | 361605.0 | 138491.0 | 29993.0 | 1877.0 | 981.0 | 10592.0 | 5568.0 | 22838.0 | 120004.0 | 93543.0 | 135437.0 | 60983 | 38.5 | 0.4599 | 67274 | 29.9 | 10.673170 |
| 108 | Sussex County | DE | 05000US10005 | -75.335495 | 38.677511 | 220251 | 181522.0 | 27436.0 | 2528.0 | 1217.0 | 1063.0 | 2460.0 | 4109.0 | 15705.0 | 50605.0 | 47459.0 | 43241.0 | 23844 | 48.7 | 0.4418 | 57734 | 29.4 | 19.165731 |
| 109 | Alachua County | FL | 05000US12001 | -82.357221 | 29.675740 | 263496 | 182119.0 | 52833.0 | 16798.0 | 1166.0 | 524.0 | 2594.0 | 3444.0 | 8861.0 | 36949.0 | 44967.0 | 63363.0 | 58215 | 31.3 | 0.5081 | 45304 | 37.8 | 13.339264 |
| 110 | Bay County | FL | 05000US12005 | -85.631348 | 30.237563 | 183974 | 151108.0 | 20489.0 | 5077.0 | 317.0 | 34.0 | 1066.0 | 1639.0 | 8458.0 | 42062.0 | 45237.0 | 29940.0 | 26569 | 40.1 | 0.4489 | 49157 | 31.8 | 13.039955 |
| 111 | Brevard County | FL | 05000US12009 | -80.700384 | 28.298275 | 579130 | 478586.0 | 57238.0 | 14164.0 | 1254.0 | 170.0 | 10763.0 | 6810.0 | 23736.0 | 121316.0 | 147381.0 | 125850.0 | 86137 | 47.3 | 0.4645 | 51184 | 30.7 | 10.627774 |
| 112 | Broward County | FL | 05000US12011 | -80.476658 | 26.193520 | 1909632 | 1163610.0 | 545324.0 | 69401.0 | 5993.0 | 1726.0 | 63148.0 | 53722.0 | 91244.0 | 360675.0 | 408687.0 | 415410.0 | 254901 | 40.3 | 0.4902 | 54212 | 36.2 | 12.772608 |
| 113 | Charlotte County | FL | 05000US12015 | -81.940858 | 26.868826 | 178465 | 160324.0 | 11548.0 | 1660.0 | 124.0 | 514.0 | 1328.0 | 3253.0 | 10503.0 | 47595.0 | 48932.0 | 34421.0 | 21977 | 58.5 | 0.4792 | 44200 | 33.3 | 12.395704 |
| 114 | Citrus County | FL | 05000US12017 | -82.524796 | 28.843628 | 143621 | NaN | NaN | NaN | NaN | NaN | NaN | 1331.0 | 11217.0 | 42315.0 | 34882.0 | 23638.0 | 23564 | 56.4 | 0.4443 | 39206 | 33.0 | 16.313049 |
| 115 | Clay County | FL | 05000US12019 | -81.858147 | 29.987116 | 208311 | 166888.0 | 21159.0 | 5746.0 | 51.0 | 397.0 | 4650.0 | 1814.0 | 6739.0 | 48783.0 | 49991.0 | 33323.0 | 20160 | 40.4 | 0.4168 | 56315 | 34.1 | 9.375422 |
| 116 | Collier County | FL | 05000US12021 | -81.400884 | 26.118713 | 365136 | 321423.0 | 26142.0 | 4794.0 | 987.0 | 0.0 | 7911.0 | 17658.0 | 18496.0 | 71481.0 | 69551.0 | 93512.0 | 41160 | 50.3 | 0.5327 | 61228 | 32.5 | 12.127836 |
| 117 | Columbia County | FL | 05000US12023 | -82.623127 | 30.221305 | 69299 | NaN | NaN | NaN | NaN | NaN | NaN | 863.0 | 5202.0 | 17142.0 | 15753.0 | 6442.0 | 9440 | 41.2 | 0.4855 | 42019 | 27.9 | 15.341730 |
| 118 | Duval County | FL | 05000US12031 | -81.648113 | 30.335245 | 926255 | 560080.0 | 271278.0 | 39936.0 | 2013.0 | 453.0 | 16293.0 | 10395.0 | 50513.0 | 171527.0 | 203635.0 | 185836.0 | 130321 | 36.1 | 0.4541 | 51980 | 28.8 | 14.515774 |
| 119 | Escambia County | FL | 05000US12033 | -87.339040 | 30.611664 | 315187 | 216766.0 | 70316.0 | 9687.0 | 1608.0 | 96.0 | 3712.0 | 2627.0 | 14573.0 | 56229.0 | 78400.0 | 56508.0 | 41672 | 37.3 | 0.4301 | 44788 | 31.1 | 13.014945 |
| 120 | Flagler County | FL | 05000US12035 | -81.286362 | 29.474894 | 108310 | NaN | NaN | NaN | NaN | NaN | NaN | 1624.0 | 5592.0 | 26324.0 | 29474.0 | 18914.0 | 12404 | 51.1 | 0.4246 | 49395 | 32.4 | 14.734574 |
| 121 | Hernando County | FL | 05000US12053 | -82.464835 | 28.567911 | 182835 | 162269.0 | 9823.0 | 2262.0 | 210.0 | 0.0 | 2822.0 | 3249.0 | 12006.0 | 48935.0 | 44967.0 | 25711.0 | 28156 | 49.0 | 0.4256 | 47253 | 34.7 | 15.050766 |
| 122 | Highlands County | FL | 05000US12055 | -81.340921 | 27.342627 | 100917 | NaN | NaN | NaN | NaN | NaN | NaN | 3128.0 | 5618.0 | 33915.0 | 22021.0 | 10022.0 | 18397 | 54.4 | 0.4504 | 36490 | 34.5 | 26.144094 |
| 123 | Hillsborough County | FL | 05000US12057 | -82.349568 | 27.906590 | 1376238 | 971753.0 | 228361.0 | 54822.0 | 4763.0 | 1279.0 | 68366.0 | 32937.0 | 66969.0 | 260737.0 | 260006.0 | 301728.0 | 204523 | 37.0 | 0.4789 | 54588 | 30.5 | 11.092797 |
| 124 | Indian River County | FL | 05000US12061 | -80.574803 | 27.700638 | 151563 | 129043.0 | 11560.0 | 1978.0 | 459.0 | 0.0 | 3617.0 | 4170.0 | 8384.0 | 32502.0 | 33662.0 | 32393.0 | 17918 | 53.0 | 0.5373 | 49072 | 32.5 | 14.886247 |
| 125 | Lake County | FL | 05000US12069 | -81.712282 | 28.764113 | 335396 | 283513.0 | 35729.0 | 6718.0 | 1217.0 | 27.0 | 1418.0 | 4904.0 | 17742.0 | 83061.0 | 80571.0 | 56517.0 | 38311 | 46.7 | 0.4094 | 50226 | 30.4 | 12.773105 |
| 126 | Lee County | FL | 05000US12071 | -81.892250 | 26.552134 | 722336 | 615028.0 | 60475.0 | 11131.0 | 1457.0 | 129.0 | 20665.0 | 19783.0 | 40922.0 | 164285.0 | 155114.0 | 152967.0 | 91858 | 48.2 | 0.4717 | 52909 | 32.0 | 13.672608 |
| 127 | Leon County | FL | 05000US12073 | -84.277800 | 30.459310 | 287822 | 177676.0 | 88212.0 | 10203.0 | 521.0 | 256.0 | 2971.0 | 1960.0 | 7531.0 | 31491.0 | 47093.0 | 81023.0 | 53318 | 30.8 | 0.4771 | 51107 | 33.1 | 7.597285 |
| 128 | Manatee County | FL | 05000US12081 | -82.365783 | 27.481386 | 375888 | 320089.0 | 33480.0 | 7143.0 | 1015.0 | 206.0 | 7070.0 | 7561.0 | 21458.0 | 87221.0 | 82575.0 | 76974.0 | 47005 | 47.7 | 0.4784 | 51748 | 34.0 | 13.976766 |
| 129 | Marion County | FL | 05000US12083 | -82.043100 | 29.202805 | 349020 | 286955.0 | 44143.0 | 4308.0 | 194.0 | 122.0 | 1951.0 | 5874.0 | 24091.0 | 99529.0 | 78526.0 | 46823.0 | 59380 | 48.6 | 0.4428 | 39383 | 30.9 | 19.528602 |
| 130 | Martin County | FL | 05000US12085 | -80.398211 | 27.079954 | 158701 | 139122.0 | 9164.0 | 1252.0 | 965.0 | 179.0 | 5813.0 | 3725.0 | 6810.0 | 30871.0 | 41857.0 | 36545.0 | 17211 | 51.5 | 0.5205 | 54620 | 34.7 | 15.237551 |
| 131 | Miami-Dade County | FL | 05000US12086 | -80.499045 | 25.610494 | 2712945 | 2019915.0 | 476506.0 | 43745.0 | 4683.0 | 1265.0 | 124482.0 | 150429.0 | 165925.0 | 542835.0 | 483546.0 | 537034.0 | 488306 | 39.9 | 0.5282 | 45935 | 38.7 | 20.921561 |
| 132 | Monroe County | FL | 05000US12087 | -81.206777 | 25.601043 | 79077 | NaN | NaN | NaN | NaN | NaN | NaN | 1296.0 | 3756.0 | 16448.0 | 17524.0 | 20034.0 | 7548 | 46.3 | 0.4610 | 65717 | 35.8 | 12.761396 |
| 133 | Nassau County | FL | 05000US12089 | -81.764929 | 30.605926 | 80622 | NaN | NaN | NaN | NaN | NaN | NaN | 585.0 | 3295.0 | 17730.0 | 19147.0 | 18017.0 | 8423 | 45.2 | 0.4410 | 71515 | 30.1 | 10.521491 |
| 134 | Okaloosa County | FL | 05000US12091 | -86.594194 | 30.665858 | 201170 | 155446.0 | 20398.0 | 5770.0 | 590.0 | 327.0 | 8785.0 | 2106.0 | 7904.0 | 35310.0 | 48611.0 | 41852.0 | 19590 | 37.2 | 0.4322 | 60026 | 29.4 | 10.403143 |
| 135 | Orange County | FL | 05000US12095 | -81.323295 | 28.514435 | 1314367 | 851713.0 | 276302.0 | 70844.0 | 1697.0 | 50.0 | 67741.0 | 34791.0 | 68209.0 | 212756.0 | 252104.0 | 294893.0 | 212247 | 34.9 | 0.4800 | 51335 | 33.3 | 12.042304 |
| 136 | Osceola County | FL | 05000US12097 | -81.139312 | 28.059027 | 336015 | 248442.0 | 40387.0 | 9588.0 | 739.0 | 142.0 | 23289.0 | 7840.0 | 17307.0 | 71686.0 | 76165.0 | 44184.0 | 49508 | 35.6 | 0.4042 | 51436 | 33.5 | 12.771475 |
| 137 | Palm Beach County | FL | 05000US12099 | -80.448673 | 26.645763 | 1443810 | 1079513.0 | 266755.0 | 38391.0 | 1811.0 | 536.0 | 26821.0 | 50604.0 | 69011.0 | 259717.0 | 292543.0 | 368824.0 | 178288 | 44.7 | 0.5179 | 57580 | 35.1 | 12.357069 |
| 138 | Pasco County | FL | 05000US12101 | -82.455707 | 28.302024 | 512368 | 452055.0 | 28037.0 | 12829.0 | 1337.0 | 190.0 | 6111.0 | 9214.0 | 28228.0 | 127733.0 | 116871.0 | 85638.0 | 66699 | 44.8 | 0.4581 | 46264 | 32.3 | 16.547222 |
| 139 | Pinellas County | FL | 05000US12103 | -82.739518 | 27.903122 | 960730 | 784811.0 | 97341.0 | 33027.0 | 3117.0 | 820.0 | 15140.0 | 14846.0 | 46810.0 | 206961.0 | 243061.0 | 215630.0 | 125474 | 48.0 | 0.4835 | 50036 | 31.1 | 14.761533 |
| 140 | Polk County | FL | 05000US12105 | -81.692783 | 27.953115 | 666149 | 525140.0 | 100793.0 | 12565.0 | 1818.0 | 306.0 | 9710.0 | 21477.0 | 41221.0 | 165401.0 | 130424.0 | 92755.0 | 106975 | 40.2 | 0.4455 | 46355 | 28.8 | 19.322172 |
| 141 | Putnam County | FL | 05000US12107 | -81.740894 | 29.606006 | 72277 | NaN | NaN | NaN | NaN | NaN | NaN | 1448.0 | 7287.0 | 19347.0 | 13284.0 | 7920.0 | 14099 | 45.3 | 0.4254 | 38239 | 30.7 | 27.272079 |
| 142 | St. Johns County | FL | 05000US12109 | -81.383914 | 29.890593 | 235087 | 208208.0 | 12331.0 | 6363.0 | 854.0 | 695.0 | 1850.0 | 1898.0 | 4771.0 | 35736.0 | 52134.0 | 71152.0 | 17568 | 43.0 | 0.4658 | 78581 | 30.0 | 7.829000 |
| 143 | St. Lucie County | FL | 05000US12111 | -80.443364 | 27.380775 | 306507 | 227646.0 | 59949.0 | 6005.0 | 1345.0 | 0.0 | 5409.0 | 8132.0 | 18212.0 | 73041.0 | 70865.0 | 45146.0 | 54771 | 45.2 | 0.4439 | 44804 | 37.3 | 14.674288 |
| 144 | Santa Rosa County | FL | 05000US12113 | -87.014255 | 30.703633 | 170497 | 146607.0 | 10669.0 | 3459.0 | 1460.0 | 506.0 | 772.0 | 1564.0 | 9139.0 | 31497.0 | 43250.0 | 32987.0 | 16505 | 40.3 | 0.4223 | 63619 | 28.4 | 6.957633 |
| 145 | Sarasota County | FL | 05000US12115 | -82.365835 | 27.184385 | 412569 | 375600.0 | 17710.0 | 6544.0 | 1005.0 | 22.0 | 4215.0 | 6026.0 | 14867.0 | 99454.0 | 95597.0 | 110253.0 | 43026 | 55.5 | 0.4744 | 54989 | 30.8 | 12.437071 |
| 146 | Seminole County | FL | 05000US12117 | -81.131980 | 28.690079 | 455479 | 354325.0 | 51494.0 | 19098.0 | 449.0 | 343.0 | 13835.0 | 3839.0 | 12523.0 | 68705.0 | 114724.0 | 118505.0 | 52799 | 39.2 | 0.4558 | 61311 | 29.9 | 8.482295 |
| 147 | Sumter County | FL | 05000US12119 | -82.074715 | 28.714294 | 123996 | NaN | NaN | NaN | NaN | NaN | NaN | 1748.0 | 5611.0 | 35180.0 | 32938.0 | 31566.0 | 11366 | 67.3 | 0.4363 | 54562 | 34.8 | 10.832159 |
| 148 | Volusia County | FL | 05000US12127 | -81.161813 | 29.057617 | 529364 | 441448.0 | 55747.0 | 9730.0 | 2528.0 | 64.0 | 7275.0 | 7300.0 | 29632.0 | 122815.0 | 134522.0 | 94387.0 | 72083 | 46.8 | 0.4612 | 45366 | 33.3 | 18.833484 |
| 149 | Walton County | FL | 05000US12131 | -86.176614 | 30.631211 | 65889 | NaN | NaN | NaN | NaN | NaN | NaN | 595.0 | 4672.0 | 12158.0 | 15793.0 | 13465.0 | 7529 | 44.9 | 0.4775 | 56246 | 31.6 | 16.833903 |
| 150 | Barrow County | GA | 05000US13013 | -83.712303 | 33.992009 | 77126 | NaN | NaN | NaN | NaN | NaN | NaN | 1788.0 | 5481.0 | 17219.0 | 15746.0 | 9276.0 | 10941 | 35.5 | 0.3880 | 54256 | 25.6 | 12.020913 |
| 151 | Bartow County | GA | 05000US13015 | -84.838188 | 34.240918 | 103807 | NaN | NaN | NaN | NaN | NaN | NaN | 2782.0 | 8067.0 | 23540.0 | 20766.0 | 13554.0 | 17105 | 37.2 | 0.4262 | 51405 | 24.8 | 12.497941 |
| 152 | Bibb County | GA | 05000US13021 | -83.694193 | 32.808844 | 152760 | 61273.0 | 82574.0 | 3089.0 | 98.0 | 0.0 | 1592.0 | 2127.0 | 10315.0 | 29533.0 | 28267.0 | 25607.0 | 38556 | 36.1 | 0.5164 | 36724 | 34.6 | 23.550926 |
| 153 | Bulloch County | GA | 05000US13031 | -81.743810 | 32.393408 | 74722 | NaN | NaN | NaN | NaN | NaN | NaN | 855.0 | 4572.0 | 10331.0 | 13569.0 | 11173.0 | 16546 | 28.4 | 0.4579 | 43982 | 31.7 | 18.424141 |
| 154 | Carroll County | GA | 05000US13045 | -85.080527 | 33.582237 | 116261 | NaN | NaN | NaN | NaN | NaN | NaN | 2089.0 | 8412.0 | 22986.0 | 21550.0 | 15453.0 | 14584 | 34.1 | 0.4031 | 51228 | 23.9 | 13.920426 |
| 155 | Catoosa County | GA | 05000US13047 | -85.137353 | 34.899393 | 66398 | NaN | NaN | NaN | NaN | NaN | NaN | 149.0 | 4613.0 | 15335.0 | 14712.0 | 10773.0 | 6033 | 40.0 | 0.4271 | 55717 | 29.3 | 13.199693 |
| 156 | Chatham County | GA | 05000US13051 | -81.091768 | 31.974756 | 289082 | 151815.0 | 114139.0 | 6369.0 | 650.0 | 558.0 | 4208.0 | 2330.0 | 15502.0 | 45036.0 | 65263.0 | 61637.0 | 43398 | 35.3 | 0.4887 | 53964 | 27.4 | 11.475246 |
| 157 | Cherokee County | GA | 05000US13057 | -84.475057 | 34.244317 | 241689 | NaN | NaN | NaN | NaN | NaN | NaN | 4311.0 | 9479.0 | 35214.0 | 50180.0 | 61072.0 | 17610 | 38.7 | 0.4147 | 77950 | 26.0 | 6.159024 |
| 158 | Clarke County | GA | 05000US13059 | -83.367130 | 33.952234 | 124707 | 78919.0 | 34893.0 | 5209.0 | 181.0 | 33.0 | 3137.0 | 1732.0 | 5335.0 | 14696.0 | 17598.0 | 28091.0 | 31693 | 27.5 | 0.5139 | 34999 | 34.0 | 15.421651 |
| 159 | Clayton County | GA | 05000US13063 | -84.412977 | 33.552242 | 279462 | NaN | NaN | NaN | NaN | NaN | NaN | 8668.0 | 13942.0 | 55868.0 | 55567.0 | 35429.0 | 56658 | 32.7 | 0.4057 | 45252 | 29.4 | 15.258729 |
| 160 | Cobb County | GA | 05000US13067 | -84.574166 | 33.939940 | 748150 | 439448.0 | 202244.0 | 39953.0 | 3133.0 | 676.0 | 36792.0 | 16578.0 | 20973.0 | 90843.0 | 130974.0 | 232294.0 | 70868 | 36.5 | 0.4483 | 70947 | 27.7 | 7.929512 |
| 161 | Columbia County | GA | 05000US13073 | -82.251342 | 33.550556 | 147450 | 108438.0 | 24938.0 | 6402.0 | 932.0 | 0.0 | 1413.0 | 1593.0 | 3186.0 | 26000.0 | 30392.0 | 34644.0 | 10627 | 36.2 | 0.4068 | 72737 | 26.3 | 16.965883 |
| 162 | Coweta County | GA | 05000US13077 | -84.762138 | 33.352896 | 140526 | NaN | NaN | NaN | NaN | NaN | NaN | 831.0 | 7943.0 | 28082.0 | 27749.0 | 27011.0 | 13165 | 39.0 | 0.4465 | 71220 | 26.8 | 8.781657 |
| 163 | DeKalb County | GA | 05000US13089 | -84.226343 | 33.770661 | 740321 | 259015.0 | 403352.0 | 45634.0 | 5253.0 | 101.0 | 9586.0 | 18932.0 | 30805.0 | 104011.0 | 124396.0 | 216819.0 | 125609 | 35.4 | 0.4946 | 56109 | 31.5 | 13.115413 |
| 164 | Dougherty County | GA | 05000US13095 | -84.214444 | 31.535068 | 90017 | NaN | NaN | NaN | NaN | NaN | NaN | 1745.0 | 5711.0 | 16770.0 | 19574.0 | 12183.0 | 25847 | 35.2 | 0.5123 | 37222 | 32.3 | 23.853024 |
| 165 | Douglas County | GA | 05000US13097 | -84.765944 | 33.699317 | 142224 | 67268.0 | 67469.0 | 2242.0 | 73.0 | 0.0 | 2812.0 | 1699.0 | 7216.0 | 28924.0 | 27918.0 | 24761.0 | 17528 | 36.0 | 0.4369 | 62445 | 26.4 | 10.666449 |
| 166 | Fayette County | GA | 05000US13113 | -84.493941 | 33.412717 | 111627 | NaN | NaN | NaN | NaN | NaN | NaN | 1057.0 | 1849.0 | 15439.0 | 20196.0 | 37209.0 | 6161 | 43.3 | 0.4401 | 80626 | 27.4 | 7.880937 |
| 167 | Floyd County | GA | 05000US13115 | -85.213730 | 34.263677 | 96560 | NaN | NaN | NaN | NaN | NaN | NaN | 2507.0 | 7003.0 | 19853.0 | 17558.0 | 15426.0 | 11739 | 38.7 | 0.4659 | 49865 | 25.7 | 18.158199 |
| 168 | Forsyth County | GA | 05000US13117 | -84.127336 | 34.225143 | 221009 | 179397.0 | 7490.0 | 25847.0 | 387.0 | 0.0 | 3346.0 | 3868.0 | 6205.0 | 23691.0 | 35513.0 | 71739.0 | 13001 | 38.7 | 0.3969 | 100909 | 24.9 | 7.033847 |
| 169 | Fulton County | GA | 05000US13121 | -84.468182 | 33.790034 | 1023336 | 455532.0 | 447187.0 | 68259.0 | 9001.0 | 879.0 | 11948.0 | 11047.0 | 38213.0 | 125877.0 | 154622.0 | 352495.0 | 155061 | 35.4 | 0.5369 | 63510 | 29.8 | 10.458777 |
| 170 | Glynn County | GA | 05000US13127 | -81.496517 | 31.212747 | 84502 | NaN | NaN | NaN | NaN | NaN | NaN | 1437.0 | 5374.0 | 15176.0 | 18179.0 | 16060.0 | 17140 | 41.0 | 0.4926 | 48926 | 29.0 | 18.613219 |
| 171 | Gwinnett County | GA | 05000US13135 | -84.022938 | 33.959101 | 907135 | 438099.0 | 248723.0 | 103900.0 | 1346.0 | 82.0 | 88207.0 | 29301.0 | 32804.0 | 132550.0 | 172483.0 | 199463.0 | 102496 | 35.2 | 0.4307 | 67155 | 31.1 | 9.450251 |
| 172 | Hall County | GA | 05000US13139 | -83.818497 | 34.317588 | 196637 | 168678.0 | 14687.0 | 3451.0 | 412.0 | 99.0 | 4766.0 | 11579.0 | 15316.0 | 37581.0 | 33288.0 | 27404.0 | 25795 | 36.5 | 0.4268 | 54917 | 27.3 | 13.438538 |
| 173 | Henry County | GA | 05000US13151 | -84.154440 | 33.452881 | 221768 | 110602.0 | 94657.0 | 7935.0 | 0.0 | 0.0 | 2709.0 | 2443.0 | 11818.0 | 39676.0 | 46471.0 | 41615.0 | 21047 | 36.5 | 0.4165 | 66905 | 25.6 | 7.386570 |
| 174 | Houston County | GA | 05000US13153 | -83.662856 | 32.458381 | 152122 | 93711.0 | 45011.0 | 4623.0 | 622.0 | 0.0 | 1763.0 | 2178.0 | 7150.0 | 22647.0 | 37813.0 | 30115.0 | 24445 | 35.4 | 0.4108 | 62493 | 27.4 | 14.493480 |
| 175 | Liberty County | GA | 05000US13179 | -81.457969 | 31.807244 | 62570 | 29415.0 | 25722.0 | 940.0 | 295.0 | 12.0 | 1916.0 | 706.0 | 2326.0 | 10858.0 | 14763.0 | 6309.0 | 7862 | 27.2 | 0.3974 | 45138 | 29.9 | 14.216626 |
| 176 | Lowndes County | GA | 05000US13185 | -83.268967 | 30.833680 | 114628 | 67078.0 | 44310.0 | 418.0 | 256.0 | 113.0 | 878.0 | 1350.0 | 6378.0 | 23796.0 | 18742.0 | 16336.0 | 24127 | 30.5 | 0.5100 | 41449 | 31.0 | 19.380433 |
| 177 | Muscogee County | GA | 05000US13215 | -84.874946 | 32.510197 | 197485 | 86603.0 | 90127.0 | 4160.0 | 1159.0 | 0.0 | 4517.0 | 2688.0 | 11025.0 | 33963.0 | 43371.0 | 35079.0 | 41236 | 33.7 | 0.5000 | 40060 | 31.6 | 19.129102 |
| 178 | Newton County | GA | 05000US13217 | -83.855189 | 33.544046 | 106999 | NaN | NaN | NaN | NaN | NaN | NaN | 2497.0 | 8403.0 | 20870.0 | 22271.0 | 11465.0 | 23299 | 36.0 | 0.4600 | 48628 | 32.0 | 14.629449 |
| 179 | Paulding County | GA | 05000US13223 | -84.866979 | 33.920903 | 155825 | NaN | NaN | NaN | NaN | NaN | NaN | 2348.0 | 6222.0 | 33021.0 | 32812.0 | 23359.0 | 13462 | 36.4 | 0.3834 | 60856 | 28.9 | 7.034874 |
| 180 | Richmond County | GA | 05000US13245 | -82.074998 | 33.361487 | 201647 | 76547.0 | 114495.0 | 4084.0 | 584.0 | 848.0 | 2376.0 | 3311.0 | 14721.0 | 40910.0 | 40681.0 | 29069.0 | 48523 | 34.0 | 0.4641 | 41419 | 30.1 | 19.079424 |
| 181 | Rockdale County | GA | 05000US13247 | -84.026370 | 33.652081 | 89355 | NaN | NaN | NaN | NaN | NaN | NaN | 1805.0 | 3902.0 | 21218.0 | 16366.0 | 14381.0 | 12616 | 38.4 | 0.4123 | 56820 | 24.7 | 18.162667 |
| 182 | Troup County | GA | 05000US13285 | -85.028360 | 33.034482 | 70005 | NaN | NaN | NaN | NaN | NaN | NaN | 1914.0 | 7008.0 | 14267.0 | 11844.0 | 9518.0 | 14790 | 36.4 | 0.4594 | 42371 | 31.6 | 25.795125 |
| 183 | Walker County | GA | 05000US13295 | -85.305385 | 34.735827 | 67896 | NaN | NaN | NaN | NaN | NaN | NaN | 1562.0 | 7979.0 | 15723.0 | 13766.0 | 7932.0 | 13758 | 41.4 | 0.4816 | 39209 | 33.7 | 19.932298 |
| 184 | Walton County | GA | 05000US13297 | -83.734215 | 33.782649 | 90184 | NaN | NaN | NaN | NaN | NaN | NaN | 1193.0 | 5914.0 | 19910.0 | 16763.0 | 13280.0 | 12057 | 38.3 | 0.4335 | 53202 | 36.3 | 16.080091 |
| 185 | Whitfield County | GA | 05000US13313 | -84.968541 | 34.801726 | 104589 | NaN | NaN | NaN | NaN | NaN | NaN | 6993.0 | 11171.0 | 18114.0 | 18613.0 | 8233.0 | 18184 | 35.1 | 0.4695 | 46399 | 24.3 | 18.977634 |
| 186 | Hawaii County | HI | 05000US15001 | -155.502443 | 19.597764 | 198449 | 64714.0 | 1586.0 | 47415.0 | 293.0 | 26900.0 | 2422.0 | 2514.0 | 6295.0 | 46005.0 | 45084.0 | 38574.0 | 30154 | 42.6 | 0.4674 | 55750 | 29.6 | 17.932338 |
| 187 | Honolulu County | HI | 05000US15003 | -158.201976 | 21.461364 | 992605 | 209223.0 | 23269.0 | 421281.0 | 1636.0 | 93165.0 | 8029.0 | 21351.0 | 31333.0 | 176857.0 | 214356.0 | 235112.0 | 81533 | 37.5 | 0.4300 | 80513 | 34.7 | 11.403472 |
| 188 | Kauai County | HI | 05000US15007 | -159.705965 | 22.012038 | 72029 | 23326.0 | 621.0 | 24200.0 | 563.0 | 5678.0 | 183.0 | 1223.0 | 1665.0 | 16790.0 | 16082.0 | 14086.0 | 4333 | 42.3 | 0.4198 | 71344 | 31.0 | 13.978634 |
| 189 | Maui County | HI | 05000US15009 | -156.601550 | 20.855931 | 165379 | 60712.0 | 422.0 | 50531.0 | 157.0 | 18797.0 | 1687.0 | 2121.0 | 4282.0 | 38072.0 | 41393.0 | 28799.0 | 13544 | 41.1 | 0.4613 | 72257 | 28.8 | 10.503246 |
| 190 | Black Hawk County | IA | 05000US19013 | -92.306059 | 42.472888 | 132904 | 113334.0 | 12012.0 | 3374.0 | 802.0 | 719.0 | 854.0 | 1499.0 | 4261.0 | 26567.0 | 26296.0 | 23543.0 | 21740 | 35.2 | 0.4828 | 50470 | 28.9 | 16.700013 |
| 191 | Dallas County | IA | 05000US19049 | -94.040706 | 41.685321 | 84516 | 76654.0 | 1003.0 | 3579.0 | 102.0 | 0.0 | 637.0 | 1486.0 | 923.0 | 9098.0 | 15841.0 | 27207.0 | 3755 | 35.2 | 0.4528 | 75899 | 31.3 | 8.875829 |
| 192 | Dubuque County | IA | 05000US19061 | -90.878771 | 42.463481 | 97003 | NaN | NaN | NaN | NaN | NaN | NaN | 410.0 | 3248.0 | 21481.0 | 19938.0 | 18922.0 | 9908 | 38.0 | 0.4751 | 60456 | 24.8 | 13.041634 |
| 193 | Johnson County | IA | 05000US19103 | -91.588812 | 41.668737 | 146547 | 119612.0 | 10314.0 | 10102.0 | 0.0 | 0.0 | 3928.0 | 1394.0 | 2670.0 | 13640.0 | 20927.0 | 46917.0 | 25733 | 30.0 | 0.4813 | 58064 | 38.3 | 8.242306 |
| 194 | Linn County | IA | 05000US19113 | -91.597673 | 42.077951 | 221661 | 198263.0 | 12247.0 | 5323.0 | 239.0 | 132.0 | 529.0 | 1417.0 | 5968.0 | 39482.0 | 48421.0 | 52954.0 | 22115 | 37.6 | 0.4330 | 64639 | 25.8 | 13.054400 |
| 195 | Polk County | IA | 05000US19153 | -93.569720 | 41.684281 | 474045 | 396575.0 | 31968.0 | 22224.0 | 1315.0 | 150.0 | 8740.0 | 9183.0 | 15514.0 | 74309.0 | 98010.0 | 112049.0 | 50855 | 35.3 | 0.4372 | 64067 | 28.0 | 11.136920 |
| 196 | Pottawattamie County | IA | 05000US19155 | -95.544905 | 41.340184 | 93582 | NaN | NaN | NaN | NaN | NaN | NaN | 298.0 | 4339.0 | 21030.0 | 22793.0 | 13517.0 | 7916 | 39.1 | 0.4568 | 55972 | 27.7 | 16.171663 |
| 197 | Scott County | IA | 05000US19163 | -90.622290 | 41.641679 | 172474 | 147577.0 | 14802.0 | 4441.0 | 554.0 | 0.0 | 2285.0 | 1089.0 | 5316.0 | 36898.0 | 35324.0 | 36493.0 | 22805 | 37.9 | 0.4585 | 54730 | 32.9 | 20.220362 |
| 198 | Story County | IA | 05000US19169 | -93.466093 | 42.037538 | 97090 | 84190.0 | 1423.0 | 8417.0 | 178.0 | 0.0 | 340.0 | 119.0 | 1052.0 | 9545.0 | 12491.0 | 28328.0 | 18802 | 26.9 | 0.4647 | 53371 | 38.9 | 7.199465 |
| 199 | Woodbury County | IA | 05000US19193 | -96.053296 | 42.393220 | 102779 | 87253.0 | 3720.0 | 3154.0 | 723.0 | 749.0 | 3083.0 | 2480.0 | 5867.0 | 19764.0 | 20874.0 | 14802.0 | 12551 | 35.1 | 0.4411 | 52324 | 27.7 | 17.190776 |
| 200 | Ada County | ID | 05000US16001 | -116.244456 | 43.447861 | 444028 | 402851.0 | 6535.0 | 11778.0 | 2026.0 | 377.0 | 6847.0 | 2911.0 | 10492.0 | 66998.0 | 102929.0 | 113442.0 | 47122 | 36.6 | 0.4696 | 61301 | 28.0 | 21.753499 |
| 201 | Bannock County | ID | 05000US16005 | -112.228986 | 42.692939 | 84377 | 74610.0 | 280.0 | 1480.0 | 3335.0 | 0.0 | 2279.0 | 1296.0 | 3098.0 | 15031.0 | 18229.0 | 14713.0 | 13683 | 34.0 | 0.4654 | 48429 | 27.1 | 13.010696 |
| 202 | Bonneville County | ID | 05000US16019 | -111.621878 | 43.395171 | 112232 | 99184.0 | 0.0 | 1521.0 | 843.0 | 0.0 | 7841.0 | 576.0 | 4067.0 | 16183.0 | 25007.0 | 21998.0 | 11958 | 33.0 | 0.4208 | 59706 | 25.2 | 8.475458 |
| 203 | Canyon County | ID | 05000US16027 | -116.708527 | 43.623051 | 211698 | 171868.0 | 48.0 | 2978.0 | 2689.0 | 835.0 | 27575.0 | 5518.0 | 12731.0 | 41785.0 | 41778.0 | 24757.0 | 31822 | 32.7 | 0.4086 | 48437 | 28.3 | 14.555032 |
| 204 | Kootenai County | ID | 05000US16055 | -116.694918 | 47.677113 | 154311 | 143862.0 | 1029.0 | 1352.0 | 2970.0 | 167.0 | 1836.0 | 685.0 | 6502.0 | 28908.0 | 41719.0 | 27414.0 | 24813 | 39.6 | 0.4217 | 51765 | 30.7 | 13.515074 |
| 205 | Twin Falls County | ID | 05000US16083 | -114.665639 | 42.352309 | 83514 | NaN | NaN | NaN | NaN | NaN | NaN | 1407.0 | 3657.0 | 14238.0 | 21566.0 | 11629.0 | 12017 | 34.7 | 0.4229 | 51210 | 34.2 | 11.663734 |
| 206 | Adams County | IL | 05000US17001 | -91.194961 | 39.986052 | 66578 | NaN | NaN | NaN | NaN | NaN | NaN | 392.0 | 3048.0 | 16117.0 | 14706.0 | 11787.0 | 8041 | 42.1 | 0.4600 | 51624 | 31.0 | 19.289414 |
| 207 | Champaign County | IL | 05000US17019 | -88.197201 | 40.139150 | 208419 | 151060.0 | 27686.0 | 21908.0 | 331.0 | 62.0 | 1244.0 | 712.0 | 4064.0 | 25018.0 | 34726.0 | 55187.0 | 37842 | 29.9 | 0.5131 | 50335 | 32.7 | 10.757916 |
| 208 | Cook County | IL | 05000US17031 | -87.645455 | 41.894294 | 5203499 | 2919459.0 | 1223763.0 | 371491.0 | 14508.0 | 1939.0 | 537416.0 | 173260.0 | 234099.0 | 832486.0 | 909459.0 | 1344758.0 | 763242 | 36.5 | 0.5049 | 60046 | 29.7 | 15.545224 |
| 209 | DeKalb County | IL | 05000US17037 | -88.768991 | 41.894613 | 104528 | 86721.0 | 7396.0 | 2439.0 | 117.0 | 0.0 | 4515.0 | 786.0 | 3740.0 | 15552.0 | 20750.0 | 19341.0 | 18120 | 30.9 | 0.4382 | 59285 | 30.3 | 9.604797 |
| 210 | DuPage County | IL | 05000US17043 | -88.086038 | 41.852058 | 929368 | 714083.0 | 45918.0 | 107270.0 | 1060.0 | 481.0 | 35093.0 | 16291.0 | 23995.0 | 117199.0 | 161110.0 | 307628.0 | 63806 | 39.0 | 0.4554 | 84908 | 29.2 | 7.904606 |
| 211 | Kane County | IL | 05000US17089 | -88.428039 | 41.939594 | 531715 | 374272.0 | 29929.0 | 21320.0 | 2405.0 | 33.0 | 91408.0 | 23571.0 | 22472.0 | 75833.0 | 100321.0 | 115853.0 | 56729 | 37.2 | 0.4488 | 73347 | 30.5 | 10.951186 |
| 212 | Kankakee County | IL | 05000US17091 | -87.861125 | 41.139494 | 110008 | 86918.0 | 17306.0 | 1023.0 | 394.0 | 0.0 | 2799.0 | 2030.0 | 6096.0 | 24885.0 | 24039.0 | 13842.0 | 14338 | 38.2 | 0.4113 | 54911 | 24.4 | 17.604416 |
| 213 | Kendall County | IL | 05000US17093 | -88.430626 | 41.588140 | 124695 | NaN | NaN | NaN | NaN | NaN | NaN | 2335.0 | 4293.0 | 16998.0 | 26126.0 | 27471.0 | 5968 | 35.2 | 0.3544 | 90482 | 31.6 | 6.863467 |
| 214 | Lake County | IL | 05000US17097 | -87.436118 | 42.326443 | 703047 | 530524.0 | 46836.0 | 52572.0 | 1206.0 | 310.0 | 52919.0 | 16075.0 | 21154.0 | 93189.0 | 115963.0 | 202633.0 | 58653 | 38.5 | 0.4909 | 83152 | 30.0 | 9.020418 |
| 215 | LaSalle County | IL | 05000US17099 | -88.885931 | 41.343341 | 110642 | NaN | NaN | NaN | NaN | NaN | NaN | 1158.0 | 6319.0 | 27210.0 | 26599.0 | 14744.0 | 14332 | 42.6 | 0.4108 | 57476 | 27.2 | 17.107233 |
| 216 | McHenry County | IL | 05000US17111 | -88.452245 | 42.324298 | 307004 | 284322.0 | 3937.0 | 8478.0 | 253.0 | 0.0 | 3878.0 | 4041.0 | 8777.0 | 54641.0 | 66576.0 | 69513.0 | 23974 | 39.7 | 0.4013 | 81063 | 29.6 | 7.037763 |
| 217 | McLean County | IL | 05000US17113 | -88.844539 | 40.494559 | 172418 | 142803.0 | 14364.0 | 8635.0 | 380.0 | 0.0 | 1723.0 | 891.0 | 2111.0 | 26611.0 | 29498.0 | 46477.0 | 22022 | 32.9 | 0.4466 | 62156 | 25.5 | 11.822863 |
| 218 | Macon County | IL | 05000US17115 | -88.961529 | 39.860237 | 106550 | 83242.0 | 13359.0 | 1089.0 | 262.0 | 13.0 | 529.0 | 1202.0 | 5192.0 | 24793.0 | 24649.0 | 15750.0 | 17702 | 41.6 | 0.4613 | 46198 | 31.2 | 16.869688 |
| 219 | Madison County | IL | 05000US17119 | -89.900195 | 38.827082 | 265759 | 233639.0 | 23424.0 | 3169.0 | 299.0 | 0.0 | 1132.0 | 986.0 | 11223.0 | 53485.0 | 69435.0 | 49032.0 | 34795 | 40.4 | 0.4343 | 56035 | 29.7 | 13.760963 |
| 220 | Peoria County | IL | 05000US17143 | -89.767358 | 40.785999 | 185006 | 135178.0 | 33901.0 | 7607.0 | 618.0 | 0.0 | 2033.0 | 1643.0 | 9115.0 | 32742.0 | 41126.0 | 37941.0 | 26789 | 37.3 | 0.4770 | 51975 | 29.2 | 19.512628 |
| 221 | Rock Island County | IL | 05000US17161 | -90.572203 | 41.468404 | 144784 | 116660.0 | 13890.0 | 3486.0 | 778.0 | 283.0 | 2819.0 | 2317.0 | 7722.0 | 28385.0 | 37898.0 | 22961.0 | 22647 | 39.0 | 0.4210 | 50948 | 24.2 | 15.355612 |
| 222 | St. Clair County | IL | 05000US17163 | -89.928546 | 38.470198 | 262759 | 168589.0 | 77768.0 | 4473.0 | 712.0 | 78.0 | 4008.0 | 2107.0 | 9961.0 | 52707.0 | 60906.0 | 49870.0 | 37795 | 38.7 | 0.4857 | 50267 | 33.0 | 19.392644 |
| 223 | Sangamon County | IL | 05000US17167 | -89.662311 | 39.756378 | 197499 | 162397.0 | 25993.0 | 3568.0 | 143.0 | 124.0 | 719.0 | 1203.0 | 7309.0 | 35861.0 | 41024.0 | 48467.0 | 27436 | 40.0 | 0.4618 | 53782 | 29.3 | 14.766330 |
| 224 | Tazewell County | IL | 05000US17179 | -89.516260 | 40.508074 | 134385 | NaN | NaN | NaN | NaN | NaN | NaN | 1283.0 | 5157.0 | 29458.0 | 34581.0 | 22478.0 | 10643 | 41.2 | 0.4104 | 60152 | 24.9 | 15.159408 |
| 225 | Vermilion County | IL | 05000US17183 | -87.726771 | 40.186740 | 78111 | NaN | NaN | NaN | NaN | NaN | NaN | 1123.0 | 4168.0 | 20700.0 | 18472.0 | 7408.0 | 15680 | 40.5 | 0.4462 | 45481 | 33.8 | 20.655851 |
| 226 | Will County | IL | 05000US17197 | -87.978456 | 41.448474 | 689529 | 505732.0 | 76425.0 | 38308.0 | 2016.0 | 515.0 | 47269.0 | 12094.0 | 21805.0 | 118831.0 | 134033.0 | 155328.0 | 47321 | 37.9 | 0.4118 | 81438 | 30.5 | 9.688777 |
| 227 | Williamson County | IL | 05000US17199 | -88.930018 | 37.730353 | 67560 | NaN | NaN | NaN | NaN | NaN | NaN | 199.0 | 3021.0 | 13083.0 | 18780.0 | 11670.0 | 10078 | 41.3 | 0.4294 | 48409 | 26.0 | 23.433355 |
| 228 | Winnebago County | IL | 05000US17201 | -89.161205 | 42.337396 | 285873 | 226518.0 | 35905.0 | 7285.0 | 217.0 | 140.0 | 5520.0 | 4577.0 | 19707.0 | 60547.0 | 64214.0 | 43587.0 | 43941 | 39.8 | 0.4558 | 49749 | 27.6 | 15.510974 |
| 229 | Allen County | IN | 05000US18003 | -85.072230 | 41.091855 | 370404 | 296773.0 | 42515.0 | 14669.0 | 461.0 | 102.0 | 4098.0 | 6248.0 | 15687.0 | 68073.0 | 77004.0 | 69172.0 | 54437 | 35.8 | 0.4479 | 51173 | 27.0 | 13.752259 |
| 230 | Bartholomew County | IN | 05000US18005 | -85.897999 | 39.205843 | 81402 | NaN | NaN | NaN | NaN | NaN | NaN | 1810.0 | 5115.0 | 15917.0 | 13351.0 | 18111.0 | 10961 | 37.5 | 0.4381 | 59102 | 24.5 | 15.806615 |
| 231 | Clark County | IN | 05000US18019 | -85.711122 | 38.476217 | 116031 | 102109.0 | 6608.0 | 457.0 | 79.0 | 0.0 | 1181.0 | 1041.0 | 6483.0 | 26941.0 | 27514.0 | 16255.0 | 11165 | 38.7 | 0.4195 | 51401 | 26.8 | 17.292578 |
| 232 | Delaware County | IN | 05000US18035 | -85.398856 | 40.227165 | 115603 | 101294.0 | 8952.0 | 1473.0 | 226.0 | 340.0 | 979.0 | 885.0 | 5322.0 | 25957.0 | 21608.0 | 16007.0 | 23511 | 35.4 | 0.4806 | 41041 | 30.5 | 17.735445 |
| 233 | Elkhart County | IN | 05000US18039 | -85.863986 | 41.600693 | 203781 | 180922.0 | 11251.0 | 2149.0 | 1040.0 | 360.0 | 3346.0 | 6052.0 | 16482.0 | 46946.0 | 31147.0 | 24192.0 | 28611 | 36.2 | 0.4073 | 54216 | 25.4 | 20.024280 |
| 234 | Floyd County | IN | 05000US18043 | -85.911474 | 38.317937 | 76990 | 69860.0 | 3954.0 | 800.0 | 183.0 | 0.0 | 252.0 | 1160.0 | 3861.0 | 16432.0 | 16055.0 | 14803.0 | 6640 | 40.4 | 0.4805 | 58586 | 29.5 | 19.113668 |
| 235 | Grant County | IN | 05000US18053 | -85.654945 | 40.515757 | 66937 | NaN | NaN | NaN | NaN | NaN | NaN | 1001.0 | 4529.0 | 17307.0 | 13197.0 | 7339.0 | 13141 | 39.9 | 0.4207 | 37117 | 30.9 | 22.726930 |
| 236 | Hamilton County | IN | 05000US18057 | -86.020586 | 40.049870 | 316373 | 276282.0 | 11797.0 | 17484.0 | 322.0 | 0.0 | 2438.0 | 935.0 | 6645.0 | 31917.0 | 46937.0 | 118839.0 | 17542 | 36.9 | 0.4352 | 89823 | 24.5 | 5.021895 |
| 237 | Hancock County | IN | 05000US18059 | -85.772904 | 39.822604 | 73717 | NaN | NaN | NaN | NaN | NaN | NaN | 439.0 | 2567.0 | 16243.0 | 15529.0 | 15236.0 | 7287 | 39.9 | 0.4027 | 67799 | 27.8 | 16.876430 |
| 238 | Hendricks County | IN | 05000US18063 | -86.510286 | 39.768749 | 160610 | NaN | NaN | NaN | NaN | NaN | NaN | 419.0 | 6873.0 | 30447.0 | 28109.0 | 40072.0 | 9431 | 36.8 | 0.3758 | 78307 | 28.5 | 9.787481 |
| 239 | Howard County | IN | 05000US18067 | -86.114118 | 40.483537 | 82568 | NaN | NaN | NaN | NaN | NaN | NaN | 617.0 | 4233.0 | 22394.0 | 17515.0 | 12186.0 | 12250 | 41.4 | 0.4556 | 45702 | 26.9 | 15.478871 |
| 240 | Johnson County | IN | 05000US18081 | -86.094600 | 39.495986 | 151982 | NaN | NaN | NaN | NaN | NaN | NaN | 1128.0 | 7171.0 | 30714.0 | 27227.0 | 32410.0 | 9860 | 37.5 | 0.4376 | 65991 | 27.4 | 13.320010 |
| 241 | Kosciusko County | IN | 05000US18085 | -85.861575 | 41.244293 | 79092 | 75552.0 | 958.0 | 407.0 | 465.0 | 0.0 | 670.0 | 1394.0 | 6180.0 | 18586.0 | 13367.0 | 11059.0 | 8715 | 38.7 | 0.4303 | 53963 | 29.2 | 19.856365 |
| 242 | Lake County | IN | 05000US18089 | -87.374337 | 41.472239 | 485846 | 299056.0 | 117223.0 | 6144.0 | 1531.0 | 95.0 | 48308.0 | 8342.0 | 24837.0 | 114124.0 | 103624.0 | 72277.0 | 77634 | 38.6 | 0.4582 | 53681 | 28.3 | 17.685033 |
| 243 | LaPorte County | IN | 05000US18091 | -86.744729 | 41.549011 | 110015 | 91255.0 | 11928.0 | 889.0 | 610.0 | 63.0 | 2249.0 | 1052.0 | 5815.0 | 30323.0 | 24621.0 | 13954.0 | 16505 | 40.8 | 0.4309 | 53507 | 25.6 | 17.317108 |
| 244 | Madison County | IN | 05000US18095 | -85.722454 | 40.166203 | 129296 | 113364.0 | 9062.0 | 554.0 | 431.0 | 0.0 | 1179.0 | 1419.0 | 8494.0 | 36318.0 | 27035.0 | 15260.0 | 22098 | 40.4 | 0.4429 | 45495 | 28.2 | 17.555529 |
| 245 | Marion County | IN | 05000US18097 | -86.135794 | 39.782976 | 941229 | 577828.0 | 260219.0 | 27885.0 | 2924.0 | 295.0 | 39113.0 | 18287.0 | 50420.0 | 173858.0 | 178149.0 | 186033.0 | 171692 | 34.3 | 0.4952 | 44874 | 30.6 | 18.093439 |
| 246 | Monroe County | IN | 05000US18105 | -86.523325 | 39.160751 | 145496 | 125427.0 | 4068.0 | 9461.0 | 310.0 | 0.0 | 848.0 | 342.0 | 5763.0 | 19840.0 | 19297.0 | 35619.0 | 33297 | 28.5 | 0.5152 | 43582 | 36.0 | 11.197329 |
| 247 | Morgan County | IN | 05000US18109 | -86.447457 | 39.482646 | 69698 | NaN | NaN | NaN | NaN | NaN | NaN | 400.0 | 3752.0 | 20735.0 | 14987.0 | 7740.0 | 7095 | 42.3 | 0.3740 | 60530 | 25.7 | 13.550481 |
| 248 | Porter County | IN | 05000US18127 | -87.071308 | 41.509922 | 167791 | 153318.0 | 5280.0 | 2055.0 | 224.0 | 129.0 | 1705.0 | 1913.0 | 6650.0 | 37808.0 | 37217.0 | 29471.0 | 11284 | 39.1 | 0.4196 | 66196 | 27.6 | 14.641349 |
| 249 | St. Joseph County | IN | 05000US18141 | -86.288159 | 41.617699 | 269141 | 209203.0 | 35405.0 | 6408.0 | 1880.0 | 190.0 | 9023.0 | 3351.0 | 14766.0 | 55910.0 | 47525.0 | 49076.0 | 41875 | 36.5 | 0.4688 | 48358 | 27.9 | 21.740788 |
| 250 | Tippecanoe County | IN | 05000US18157 | -86.893943 | 40.389260 | 188059 | 152857.0 | 9666.0 | 15385.0 | 268.0 | 200.0 | 6020.0 | 2045.0 | 5789.0 | 27194.0 | 29256.0 | 38232.0 | 32247 | 28.2 | 0.4730 | 51361 | 34.4 | 11.892406 |
| 251 | Vanderburgh County | IN | 05000US18163 | -87.586166 | 38.020070 | 181721 | 155227.0 | 15804.0 | 1990.0 | 310.0 | 1210.0 | 1808.0 | 1015.0 | 12387.0 | 37585.0 | 39122.0 | 31718.0 | 31154 | 37.7 | 0.4625 | 46064 | 29.6 | 19.747092 |
| 252 | Vigo County | IN | 05000US18167 | -87.390375 | 39.429143 | 107931 | 94021.0 | 6808.0 | 2044.0 | 427.0 | 47.0 | 686.0 | 1227.0 | 5676.0 | 21572.0 | 21886.0 | 17737.0 | 16294 | 35.7 | 0.4289 | 43910 | 36.7 | 14.719302 |
| 253 | Wayne County | IN | 05000US18177 | -85.006735 | 39.863091 | 66568 | NaN | NaN | NaN | NaN | NaN | NaN | 771.0 | 3204.0 | 19100.0 | 13186.0 | 7987.0 | 10426 | 40.4 | 0.4303 | 43401 | 26.3 | 24.952898 |
| 254 | Butler County | KS | 05000US20015 | -96.838762 | 37.773681 | 67025 | NaN | NaN | NaN | NaN | NaN | NaN | 73.0 | 3201.0 | 11978.0 | 15557.0 | 12768.0 | 4857 | 38.4 | 0.4117 | 60182 | 25.5 | 14.922113 |
| 255 | Douglas County | KS | 05000US20045 | -95.290529 | 38.896573 | 119440 | 97894.0 | 4866.0 | 6249.0 | 1800.0 | 26.0 | 2960.0 | 600.0 | 2559.0 | 12743.0 | 16972.0 | 35978.0 | 19125 | 30.0 | 0.4659 | 56345 | 33.2 | 10.804880 |
| 256 | Johnson County | KS | 05000US20091 | -94.822330 | 38.883907 | 584451 | 505785.0 | 25393.0 | 27644.0 | 1260.0 | 332.0 | 5627.0 | 4901.0 | 10117.0 | 55924.0 | 102829.0 | 217267.0 | 32437 | 37.6 | 0.4457 | 80553 | 27.2 | 5.911190 |
| 257 | Leavenworth County | KS | 05000US20103 | -95.038977 | 39.189511 | 80204 | 64838.0 | 7435.0 | 1697.0 | 463.0 | 295.0 | 3120.0 | 208.0 | 2204.0 | 15327.0 | 17439.0 | 18116.0 | 5777 | 36.7 | 0.4202 | 68299 | 24.5 | 12.037241 |
| 258 | Riley County | KS | 05000US20161 | -96.727489 | 39.291211 | 73343 | 60227.0 | 4370.0 | 3820.0 | 182.0 | 264.0 | 998.0 | NaN | NaN | NaN | NaN | NaN | 12529 | 25.2 | 0.4603 | 50737 | 29.9 | 7.922266 |
| 259 | Sedgwick County | KS | 05000US20173 | -97.459451 | 37.683807 | 511995 | 400297.0 | 42474.0 | 21236.0 | 4630.0 | 0.0 | 18824.0 | 7863.0 | 23613.0 | 85423.0 | 104750.0 | 103878.0 | 75984 | 35.1 | 0.4489 | 52193 | 27.6 | 15.302199 |
| 260 | Shawnee County | KS | 05000US20177 | -95.755664 | 39.041805 | 178146 | 143411.0 | 14949.0 | 2374.0 | 1773.0 | 127.0 | 9288.0 | 2154.0 | 6369.0 | 39983.0 | 35016.0 | 35972.0 | 17150 | 38.4 | 0.4436 | 55710 | 27.0 | 21.939773 |
| 261 | Wyandotte County | KS | 05000US20209 | -94.763087 | 39.115384 | 163831 | 96425.0 | 37308.0 | 6063.0 | 1335.0 | 0.0 | 15480.0 | 7094.0 | 13040.0 | 33589.0 | 28248.0 | 18506.0 | 30865 | 33.9 | 0.4176 | 43129 | 28.2 | 23.140934 |
| 262 | Boone County | KY | 05000US21015 | -84.731444 | 38.974595 | 128536 | NaN | NaN | NaN | NaN | NaN | NaN | 726.0 | 3699.0 | 20816.0 | 31063.0 | 25263.0 | 9664 | 37.0 | 0.4280 | 72374 | 27.6 | 8.593567 |
| 263 | Bullitt County | KY | 05000US21029 | -85.703036 | 37.969572 | 79151 | NaN | NaN | NaN | NaN | NaN | NaN | 721.0 | 5831.0 | 21501.0 | 16811.0 | 8304.0 | 8372 | 39.9 | 0.4168 | 65359 | 27.6 | 14.381651 |
| 264 | Campbell County | KY | 05000US21037 | -84.379583 | 38.946981 | 92211 | NaN | NaN | NaN | NaN | NaN | NaN | 1069.0 | 2797.0 | 18819.0 | 16450.0 | 22547.0 | 11420 | 37.7 | 0.4355 | 62536 | 28.0 | 14.319410 |
| 265 | Christian County | KY | 05000US21047 | -87.493554 | 36.893388 | 72351 | 51567.0 | 16784.0 | 420.0 | 251.0 | 433.0 | 1227.0 | 452.0 | 3709.0 | 13415.0 | 16118.0 | 6308.0 | 14409 | 28.5 | 0.4613 | 41140 | 29.4 | 24.720353 |
| 266 | Daviess County | KY | 05000US21059 | -87.087139 | 37.731671 | 99674 | NaN | NaN | NaN | NaN | NaN | NaN | 1175.0 | 4197.0 | 20840.0 | 23587.0 | 15602.0 | 16694 | 38.9 | 0.4748 | 51764 | 27.8 | 16.195211 |
| 267 | Fayette County | KY | 05000US21067 | -84.458443 | 38.040157 | 318449 | 238917.0 | 46097.0 | 11699.0 | 1080.0 | 201.0 | 10307.0 | 5269.0 | 11751.0 | 41326.0 | 54655.0 | 90182.0 | 54232 | 34.4 | 0.4942 | 53178 | 28.9 | 11.121861 |
| 268 | Hardin County | KY | 05000US21093 | -85.963183 | 37.695836 | 107316 | 84280.0 | 13475.0 | 1851.0 | 175.0 | 275.0 | 2024.0 | 1037.0 | 4474.0 | 20651.0 | 26804.0 | 16654.0 | 14717 | 37.3 | 0.4394 | 52148 | 25.2 | 14.461748 |
| 269 | Jefferson County | KY | 05000US21111 | -85.657624 | 38.189533 | 765352 | 551075.0 | 165658.0 | 21038.0 | 1477.0 | 409.0 | 5410.0 | 8480.0 | 37311.0 | 137233.0 | 169097.0 | 168689.0 | 106861 | 38.3 | 0.4767 | 51991 | 27.8 | 13.278542 |
| 270 | Kenton County | KY | 05000US21117 | -84.533492 | 38.930477 | 164945 | 149375.0 | 7334.0 | 1922.0 | 241.0 | 0.0 | 1667.0 | 2509.0 | 8243.0 | 30245.0 | 34821.0 | 34126.0 | 20482 | 37.4 | 0.4235 | 62182 | 25.6 | 10.556682 |
| 271 | McCracken County | KY | 05000US21145 | -88.712378 | 37.053688 | 65162 | NaN | NaN | NaN | NaN | NaN | NaN | 684.0 | 3644.0 | 12752.0 | 18176.0 | 9231.0 | 12791 | 40.7 | 0.5034 | 39048 | 29.0 | 22.248029 |
| 272 | Madison County | KY | 05000US21151 | -84.277008 | 37.723528 | 89547 | NaN | NaN | NaN | NaN | NaN | NaN | 1245.0 | 4483.0 | 15985.0 | 15785.0 | 15612.0 | 18998 | 34.0 | 0.4557 | 43840 | 34.5 | 18.862427 |
| 273 | Oldham County | KY | 05000US21185 | -85.456059 | 38.400046 | 65560 | 60007.0 | 1848.0 | 834.0 | 361.0 | 0.0 | 523.0 | 241.0 | 1666.0 | 8908.0 | 14267.0 | 17663.0 | 3917 | 38.4 | 0.4344 | 90341 | 30.7 | 5.717658 |
| 274 | Warren County | KY | 05000US21227 | -86.423579 | 36.995634 | 125532 | 103029.0 | 11775.0 | 4718.0 | 642.0 | 0.0 | 2655.0 | 2058.0 | 5526.0 | 20225.0 | 22392.0 | 24066.0 | 22748 | 33.1 | 0.4872 | 46686 | 28.1 | 11.864514 |
| 275 | Ascension Parish | LA | 05000US22005 | -90.910023 | 30.202946 | 121587 | NaN | NaN | NaN | NaN | NaN | NaN | 1698.0 | 6975.0 | 25133.0 | 23758.0 | 19153.0 | 11764 | 35.1 | 0.4165 | 76581 | 26.3 | 8.358974 |
| 276 | Bossier Parish | LA | 05000US22015 | -93.617977 | 32.696202 | 126057 | 91878.0 | 28446.0 | 1906.0 | 275.0 | 220.0 | 773.0 | 1939.0 | 6925.0 | 23886.0 | 27462.0 | 21579.0 | 23032 | 34.7 | 0.4485 | 48163 | 32.6 | 18.527962 |
| 277 | Caddo Parish | LA | 05000US22017 | -93.882423 | 32.577195 | 248851 | 114096.0 | 123912.0 | 3212.0 | 552.0 | 0.0 | 3470.0 | 2731.0 | 16516.0 | 57001.0 | 49289.0 | 37049.0 | 65090 | 36.9 | 0.5444 | 37104 | 36.8 | 27.304935 |
| 278 | Calcasieu Parish | LA | 05000US22019 | -93.358015 | 30.229559 | 200601 | 140157.0 | 50582.0 | 2284.0 | 747.0 | 0.0 | 2541.0 | 3604.0 | 13673.0 | 44163.0 | 41870.0 | 26954.0 | 42143 | 36.2 | 0.4777 | 45962 | 29.0 | 25.956458 |
| 279 | East Baton Rouge Parish | LA | 05000US22033 | -91.093174 | 30.544002 | 447037 | 210180.0 | 205707.0 | 14660.0 | 419.0 | 0.0 | 8592.0 | 4611.0 | 17875.0 | 78360.0 | 81858.0 | 97181.0 | 86232 | 33.3 | 0.5162 | 50508 | 30.1 | 14.590508 |
| 280 | Iberia Parish | LA | 05000US22045 | -91.842706 | 29.606013 | 73273 | NaN | NaN | NaN | NaN | NaN | NaN | 2267.0 | 6003.0 | 19233.0 | 10114.0 | 6924.0 | 16982 | 36.8 | 0.4309 | 41424 | 33.1 | 23.612805 |
| 281 | Jefferson Parish | LA | 05000US22051 | -90.036231 | 29.503300 | 436523 | 271283.0 | 117081.0 | 18643.0 | 1899.0 | 0.0 | 18732.0 | 12571.0 | 28356.0 | 97084.0 | 83620.0 | 78274.0 | 67693 | 39.0 | 0.4849 | 49678 | 33.2 | 22.190264 |
| 282 | Lafayette Parish | LA | 05000US22055 | -92.064170 | 30.206506 | 241398 | 168941.0 | 61221.0 | 4038.0 | 401.0 | 0.0 | 1594.0 | 3340.0 | 12377.0 | 48517.0 | 43174.0 | 51322.0 | 45278 | 34.1 | 0.4956 | 49969 | 29.0 | 12.758892 |
| 283 | Lafourche Parish | LA | 05000US22057 | -90.394849 | 29.491992 | 98305 | 77516.0 | 13471.0 | 504.0 | 2473.0 | 73.0 | 2617.0 | 3200.0 | 9036.0 | 26143.0 | 13820.0 | 11675.0 | 17167 | 38.0 | 0.4359 | 51772 | 28.2 | 22.113498 |
| 284 | Livingston Parish | LA | 05000US22063 | -90.727474 | 30.440419 | 140138 | NaN | NaN | NaN | NaN | NaN | NaN | 2436.0 | 9736.0 | 37043.0 | 24425.0 | 15615.0 | 18467 | 36.3 | 0.4305 | 56534 | 23.1 | 14.903431 |
| 285 | Orleans Parish | LA | 05000US22071 | -89.939007 | 30.068636 | 391495 | 133434.0 | 233836.0 | 11721.0 | 434.0 | 97.0 | 4750.0 | 7877.0 | 28169.0 | 59915.0 | 70401.0 | 105791.0 | 88916 | 35.7 | 0.5690 | 38681 | 35.5 | 23.004114 |
| 286 | Ouachita Parish | LA | 05000US22073 | -92.154798 | 32.477495 | 156983 | NaN | NaN | NaN | NaN | NaN | NaN | 1498.0 | 12583.0 | 38011.0 | 26777.0 | 21265.0 | 37704 | 35.6 | 0.5058 | 37275 | 34.0 | 27.426448 |
| 287 | Rapides Parish | LA | 05000US22079 | -92.535953 | 31.193204 | 132424 | 84072.0 | 42337.0 | 2230.0 | 701.0 | 0.0 | 419.0 | 2645.0 | 7981.0 | 32382.0 | 24186.0 | 18526.0 | 25096 | 37.4 | 0.4997 | 42582 | 33.1 | 21.566656 |
| 288 | St. Landry Parish | LA | 05000US22097 | -91.989274 | 30.583441 | 83883 | NaN | NaN | NaN | NaN | NaN | NaN | 2591.0 | 7331.0 | 23070.0 | 9909.0 | 7712.0 | 22384 | 36.5 | 0.4942 | 31207 | 35.2 | 33.416936 |
| 289 | St. Tammany Parish | LA | 05000US22103 | -89.951962 | 30.410022 | 253602 | 210347.0 | 27933.0 | 3793.0 | 1660.0 | 274.0 | 3516.0 | 2130.0 | 13037.0 | 46570.0 | 55250.0 | 53821.0 | 21281 | 39.7 | 0.4543 | 64639 | 30.0 | 12.131663 |
| 290 | Tangipahoa Parish | LA | 05000US22105 | -90.406633 | 30.621581 | 130710 | 87647.0 | 37463.0 | 730.0 | 481.0 | 0.0 | 577.0 | 1279.0 | 13264.0 | 27623.0 | 22153.0 | 16816.0 | 25960 | 35.9 | 0.4788 | 48162 | 37.7 | 16.793701 |
| 291 | Terrebonne Parish | LA | 05000US22109 | -90.844190 | 29.333266 | 113220 | 79883.0 | 22901.0 | 571.0 | 6376.0 | 0.0 | 1450.0 | 3274.0 | 7880.0 | 29616.0 | 18061.0 | 10872.0 | 26021 | 35.5 | 0.4809 | 46026 | 33.0 | 22.896614 |
| 292 | Barnstable County | MA | 05000US25001 | -70.211083 | 41.798819 | 214276 | 196365.0 | 5175.0 | 2951.0 | 914.0 | 1034.0 | 2794.0 | 1053.0 | 4563.0 | 42874.0 | 43873.0 | 71991.0 | 13717 | 53.1 | 0.4678 | 67898 | 33.6 | 10.608974 |
| 293 | Berkshire County | MA | 05000US25003 | -73.213948 | 42.375314 | 126903 | 116668.0 | 3428.0 | 2049.0 | 260.0 | 67.0 | 1200.0 | 1198.0 | 5759.0 | 28442.0 | 24614.0 | 31121.0 | 11869 | 46.7 | 0.4463 | 58418 | 27.4 | 13.987898 |
| 294 | Bristol County | MA | 05000US25005 | -71.087062 | 41.748576 | 558324 | 469087.0 | 23337.0 | 13414.0 | 105.0 | 305.0 | 38545.0 | 20451.0 | 25365.0 | 112509.0 | 107439.0 | 113980.0 | 57480 | 40.8 | 0.4580 | 66027 | 29.0 | 15.010363 |
| 295 | Essex County | MA | 05000US25009 | -70.865107 | 42.642711 | 779018 | 635443.0 | 30824.0 | 26984.0 | 1362.0 | 366.0 | 63750.0 | 20480.0 | 28643.0 | 133541.0 | 137304.0 | 205985.0 | 82528 | 41.3 | 0.4801 | 73901 | 31.5 | 12.424047 |
| 296 | Franklin County | MA | 05000US25011 | -72.591655 | 42.583791 | 70382 | 65664.0 | 692.0 | 710.0 | 262.0 | 293.0 | 583.0 | 264.0 | 2414.0 | 15551.0 | 15071.0 | 19419.0 | 6051 | 45.9 | 0.4626 | 57106 | 30.2 | 16.713101 |
| 297 | Hampden County | MA | 05000US25013 | -72.635648 | 42.136197 | 468467 | 388013.0 | 40874.0 | 10440.0 | 1009.0 | 0.0 | 14956.0 | 12049.0 | 25832.0 | 93476.0 | 93327.0 | 83072.0 | 75434 | 38.9 | 0.4518 | 51544 | 31.6 | 18.102372 |
| 298 | Hampshire County | MA | 05000US25015 | -72.663694 | 42.339459 | 161816 | 143231.0 | 3958.0 | 7242.0 | 72.0 | 265.0 | 1101.0 | 1223.0 | 3526.0 | 24640.0 | 23802.0 | 45885.0 | 17078 | 36.6 | 0.4638 | 64354 | 34.3 | 9.649801 |
| 299 | Middlesex County | MA | 05000US25017 | -71.396507 | 42.479477 | 1589774 | 1220690.0 | 86605.0 | 187448.0 | 2454.0 | 517.0 | 52376.0 | 29494.0 | 35692.0 | 215733.0 | 205626.0 | 611606.0 | 118943 | 38.5 | 0.4566 | 95249 | 28.4 | 9.619050 |
| 300 | Norfolk County | MA | 05000US25021 | -71.179875 | 42.169702 | 697181 | 546568.0 | 45901.0 | 74523.0 | 843.0 | 215.0 | 12800.0 | 6857.0 | 18294.0 | 99200.0 | 101633.0 | 256115.0 | 40963 | 41.0 | 0.4840 | 92696 | 29.7 | 8.249523 |
| 301 | Plymouth County | MA | 05000US25023 | -70.741942 | 41.987196 | 513565 | 430007.0 | 45757.0 | 6215.0 | 688.0 | 152.0 | 20248.0 | 6604.0 | 17093.0 | 105106.0 | 95007.0 | 128552.0 | 39173 | 42.4 | 0.4362 | 82087 | 29.2 | 10.403788 |
| 302 | Suffolk County | MA | 05000US25025 | -71.020173 | 42.331960 | 784230 | 432065.0 | 179093.0 | 68847.0 | 2715.0 | 252.0 | 42262.0 | 30838.0 | 35051.0 | 123792.0 | 96388.0 | 242704.0 | 146698 | 32.7 | 0.5330 | 61796 | 30.6 | 13.076046 |
| 303 | Worcester County | MA | 05000US25027 | -71.940282 | 42.311693 | 819589 | 683124.0 | 39855.0 | 39751.0 | 1532.0 | 292.0 | 33956.0 | 11440.0 | 37402.0 | 157340.0 | 147222.0 | 201190.0 | 75347 | 40.1 | 0.4512 | 69295 | 29.3 | 12.897554 |
| 304 | Allegany County | MD | 05000US24001 | -78.703108 | 39.612309 | 72130 | 63802.0 | 6374.0 | 619.0 | 150.0 | 27.0 | 64.0 | 443.0 | 3333.0 | 20514.0 | 16166.0 | 9040.0 | 10294 | 40.8 | 0.4265 | 45606 | 29.8 | 20.175312 |
| 305 | Anne Arundel County | MD | 05000US24003 | -76.560511 | 38.993374 | 568346 | 416095.0 | 91607.0 | 20626.0 | 1034.0 | 53.0 | 13384.0 | 3907.0 | 20570.0 | 93197.0 | 106153.0 | 165472.0 | 38160 | 38.0 | 0.4147 | 96483 | 28.6 | 6.972980 |
| 306 | Baltimore County | MD | 05000US24005 | -76.616569 | 39.443167 | 831026 | 512637.0 | 238123.0 | 49490.0 | 2431.0 | 579.0 | 6408.0 | 10551.0 | 30920.0 | 154668.0 | 155005.0 | 219510.0 | 72939 | 39.4 | 0.4352 | 72764 | 28.5 | 10.739132 |
| 307 | Calvert County | MD | 05000US24009 | -76.525864 | 38.521358 | 91251 | 73623.0 | 11839.0 | 1709.0 | 0.0 | 0.0 | 1064.0 | 448.0 | 2863.0 | 19788.0 | 20191.0 | 18826.0 | 4395 | 40.6 | 0.3644 | 98732 | 27.6 | 8.660665 |
| 308 | Carroll County | MD | 05000US24013 | -77.015512 | 39.563189 | 167656 | 153931.0 | 5631.0 | 3078.0 | 407.0 | 104.0 | 555.0 | 963.0 | 7277.0 | 35491.0 | 31823.0 | 39753.0 | 7331 | 43.2 | 0.3904 | 90343 | 28.6 | 12.511961 |
| 309 | Cecil County | MD | 05000US24015 | -75.941584 | 39.562352 | 102603 | NaN | NaN | NaN | NaN | NaN | NaN | 843.0 | 6292.0 | 25122.0 | 20505.0 | 16887.0 | 9877 | 40.0 | 0.4041 | 74221 | 31.6 | 10.649936 |
| 310 | Charles County | MD | 05000US24017 | -77.015427 | 38.472853 | 157705 | 71927.0 | 71324.0 | 4466.0 | 246.0 | 0.0 | 1100.0 | 1571.0 | 4076.0 | 36816.0 | 32951.0 | 28837.0 | 10563 | 38.1 | 0.3904 | 95735 | 31.0 | 12.852009 |
| 311 | Frederick County | MD | 05000US24021 | -77.397627 | 39.470427 | 247591 | 200808.0 | 25712.0 | 11679.0 | 1172.0 | 58.0 | 2201.0 | 4224.0 | 8354.0 | 41846.0 | 43749.0 | 68585.0 | 17448 | 39.3 | 0.4057 | 90043 | 29.3 | 9.528953 |
| 312 | Harford County | MD | 05000US24025 | -76.299789 | 39.537429 | 251032 | 198064.0 | 33432.0 | 5848.0 | 476.0 | 78.0 | 2935.0 | 3854.0 | 8781.0 | 46114.0 | 50534.0 | 62906.0 | 17946 | 41.2 | 0.4133 | 84175 | 30.5 | 10.479997 |
| 313 | Howard County | MD | 05000US24027 | -76.924406 | 39.252264 | 317233 | 186168.0 | 58167.0 | 57006.0 | 405.0 | 56.0 | 2907.0 | 3348.0 | 4605.0 | 30124.0 | 41966.0 | 131537.0 | 14639 | 38.6 | 0.4010 | 120941 | 27.1 | 4.451671 |
| 314 | Montgomery County | MD | 05000US24031 | -77.203063 | 39.137382 | 1043863 | 574964.0 | 191046.0 | 154518.0 | 2053.0 | 239.0 | 77581.0 | 28988.0 | 23024.0 | 102471.0 | 131934.0 | 423556.0 | 69755 | 39.0 | 0.4613 | 99763 | 30.0 | 6.842179 |
| 315 | Prince George's County | MD | 05000US24033 | -76.847272 | 38.825880 | 908049 | 159377.0 | 571454.0 | 38290.0 | 3802.0 | 287.0 | 106267.0 | 38652.0 | 39999.0 | 159212.0 | 171427.0 | 195356.0 | 81035 | 36.7 | 0.3894 | 79184 | 29.7 | 10.247356 |
| 316 | St. Mary's County | MD | 05000US24037 | -76.534270 | 38.222666 | 112587 | NaN | NaN | NaN | NaN | NaN | NaN | 1173.0 | 6912.0 | 22272.0 | 22592.0 | 19369.0 | 10839 | 35.9 | 0.3917 | 78195 | 30.0 | 15.997873 |
| 317 | Washington County | MD | 05000US24043 | -77.814671 | 39.603621 | 150292 | 126158.0 | 16125.0 | 2009.0 | 228.0 | 253.0 | 313.0 | 2441.0 | 10760.0 | 38948.0 | 29853.0 | 21353.0 | 19764 | 40.5 | 0.4461 | 54250 | 27.9 | 20.007524 |
| 318 | Wicomico County | MD | 05000US24045 | -75.632206 | 38.367389 | 102577 | 69068.0 | 26015.0 | 3530.0 | 30.0 | 108.0 | 933.0 | 1510.0 | 4594.0 | 23577.0 | 17959.0 | 16126.0 | 19639 | 35.8 | 0.4305 | 50844 | 35.4 | 17.767988 |
| 319 | Baltimore city | MD | 05000US24510 | -76.610516 | 39.300213 | 614664 | 189260.0 | 384465.0 | 15149.0 | 1800.0 | 604.0 | 9016.0 | 13283.0 | 41757.0 | 125266.0 | 104018.0 | 130304.0 | 130053 | 34.9 | 0.5211 | 47350 | 29.6 | 20.636233 |
| 320 | Androscoggin County | ME | 05000US23001 | -70.207435 | 44.167681 | 107319 | NaN | NaN | NaN | NaN | NaN | NaN | 820.0 | 4522.0 | 28522.0 | 22964.0 | 16219.0 | 12573 | 40.4 | 0.4154 | 49081 | 28.5 | 14.317519 |
| 321 | Aroostook County | ME | 05000US23003 | -68.649410 | 46.727057 | 67959 | 64534.0 | 904.0 | 292.0 | 850.0 | 13.0 | 84.0 | 1184.0 | 3353.0 | 17626.0 | 16063.0 | 9962.0 | 11101 | 47.5 | 0.4534 | 39450 | 28.5 | 24.667442 |
| 322 | Cumberland County | ME | 05000US23005 | -70.330375 | 43.808348 | 292041 | 268659.0 | 8502.0 | 6084.0 | 996.0 | 0.0 | 661.0 | 1586.0 | 6982.0 | 49944.0 | 53968.0 | 95726.0 | 30276 | 42.2 | 0.4572 | 65913 | 30.1 | 9.361367 |
| 323 | Kennebec County | ME | 05000US23011 | -69.765764 | 44.417012 | 120569 | 115205.0 | 1336.0 | 1313.0 | 607.0 | 183.0 | 218.0 | 1483.0 | 4829.0 | 29362.0 | 26068.0 | 22904.0 | 18447 | 43.3 | 0.4355 | 51573 | 27.3 | 15.101170 |
| 324 | Penobscot County | ME | 05000US23019 | -68.657487 | 45.390602 | 151806 | 143639.0 | 811.0 | 1404.0 | 1940.0 | 0.0 | 338.0 | 492.0 | 4821.0 | 38783.0 | 34573.0 | 26852.0 | 22236 | 42.0 | 0.4529 | 47328 | 29.8 | 13.610765 |
| 325 | York County | ME | 05000US23031 | -70.670402 | 43.427239 | 202343 | NaN | NaN | NaN | NaN | NaN | NaN | 1871.0 | 6912.0 | 46320.0 | 46387.0 | 45129.0 | 14355 | 44.9 | 0.4357 | 60863 | 27.2 | 13.550546 |
| 326 | Allegan County | MI | 05000US26005 | -86.634745 | 42.595788 | 115548 | 108703.0 | 1445.0 | 417.0 | 307.0 | 0.0 | 1013.0 | 963.0 | 3727.0 | 29442.0 | 24752.0 | 17114.0 | 8777 | 39.4 | 0.3837 | 57846 | 25.6 | 19.230861 |
| 327 | Bay County | MI | 05000US26017 | -83.978701 | 43.699711 | 104747 | NaN | NaN | NaN | NaN | NaN | NaN | 948.0 | 5252.0 | 26412.0 | 28257.0 | 13348.0 | 16403 | 43.1 | 0.4429 | 44756 | 29.7 | 16.703893 |
| 328 | Berrien County | MI | 05000US26021 | -86.741822 | 41.792639 | 154010 | 120388.0 | 22043.0 | 2977.0 | 413.0 | 16.0 | 2443.0 | 2060.0 | 7921.0 | 29052.0 | 36455.0 | 30250.0 | 24806 | 41.6 | 0.4783 | 47083 | 29.4 | 18.521539 |
| 329 | Calhoun County | MI | 05000US26025 | -85.012385 | 42.242990 | 134386 | 110181.0 | 14071.0 | 2622.0 | 539.0 | 0.0 | 848.0 | 1744.0 | 5827.0 | 34381.0 | 29390.0 | 18352.0 | 22036 | 39.6 | 0.4545 | 45902 | 31.9 | 20.454545 |
| 330 | Clinton County | MI | 05000US26037 | -84.591695 | 42.950455 | 77888 | NaN | NaN | NaN | NaN | NaN | NaN | 579.0 | 1858.0 | 14857.0 | 18196.0 | 17398.0 | 7476 | 40.1 | 0.4254 | 65730 | 25.0 | 11.539525 |
| 331 | Eaton County | MI | 05000US26045 | -84.846524 | 42.589614 | 109160 | 95114.0 | 6008.0 | 2049.0 | 572.0 | 178.0 | 1071.0 | 810.0 | 3794.0 | 21045.0 | 30020.0 | 19445.0 | 13573 | 40.9 | 0.4152 | 56211 | 26.8 | 15.134554 |
| 332 | Genesee County | MI | 05000US26049 | -83.706372 | 43.021077 | 408615 | 304971.0 | 81377.0 | 4111.0 | 1737.0 | 305.0 | 2400.0 | 3055.0 | 20269.0 | 90709.0 | 106050.0 | 55990.0 | 81834 | 39.9 | 0.4738 | 43955 | 33.6 | 20.941431 |
| 333 | Grand Traverse County | MI | 05000US26055 | -85.553848 | 44.718688 | 92084 | NaN | NaN | NaN | NaN | NaN | NaN | 560.0 | 2572.0 | 16555.0 | 24578.0 | 21783.0 | 9739 | 43.4 | 0.4709 | 58532 | 29.1 | 14.111994 |
| 334 | Ingham County | MI | 05000US26065 | -84.373811 | 42.603534 | 288051 | 213921.0 | 33628.0 | 19021.0 | 993.0 | 187.0 | 5347.0 | 2516.0 | 9338.0 | 38545.0 | 54666.0 | 66044.0 | 54799 | 31.8 | 0.4651 | 49139 | 31.3 | 14.744272 |
| 335 | Isabella County | MI | 05000US26073 | -84.839424 | 43.645233 | 71282 | 62681.0 | 1517.0 | 1392.0 | 1337.0 | 0.0 | 424.0 | 516.0 | 1784.0 | 11139.0 | 12398.0 | 11608.0 | 15601 | 27.8 | 0.4938 | 41868 | 40.3 | 13.612040 |
| 336 | Jackson County | MI | 05000US26075 | -84.420868 | 42.248474 | 158460 | 138283.0 | 13482.0 | 1260.0 | 676.0 | 116.0 | 810.0 | 1324.0 | 7933.0 | 31409.0 | 44255.0 | 24079.0 | 19592 | 41.2 | 0.4421 | 50009 | 29.5 | 17.147000 |
| 337 | Kalamazoo County | MI | 05000US26077 | -85.532854 | 42.246266 | 261654 | 212319.0 | 28131.0 | 6828.0 | 373.0 | 0.0 | 2359.0 | 2213.0 | 7117.0 | 37289.0 | 53475.0 | 61780.0 | 43413 | 34.7 | 0.4638 | 53138 | 31.1 | 10.276931 |
| 338 | Kent County | MI | 05000US26081 | -85.547446 | 43.032497 | 642173 | 514715.0 | 59715.0 | 18849.0 | 1930.0 | 67.0 | 21076.0 | 13897.0 | 23270.0 | 103835.0 | 129279.0 | 146606.0 | 75539 | 35.1 | 0.4573 | 59668 | 27.6 | 10.943842 |
| 339 | Lapeer County | MI | 05000US26087 | -83.224325 | 43.088633 | 88340 | 84352.0 | 1009.0 | 509.0 | 203.0 | 56.0 | 522.0 | 1150.0 | 4652.0 | 24180.0 | 20907.0 | 11095.0 | 10064 | 44.6 | 0.4330 | 54309 | 33.7 | 16.170354 |
| 340 | Lenawee County | MI | 05000US26091 | -84.066853 | 41.895915 | 98504 | 91291.0 | 2998.0 | 256.0 | 702.0 | 0.0 | 663.0 | 1116.0 | 4261.0 | 25187.0 | 21919.0 | 15097.0 | 11686 | 42.1 | 0.4336 | 51918 | 26.9 | 15.425743 |
| 341 | Livingston County | MI | 05000US26093 | -83.911718 | 42.602532 | 188624 | 182458.0 | 1273.0 | 1730.0 | 291.0 | 79.0 | 267.0 | 1062.0 | 4742.0 | 36223.0 | 43413.0 | 44858.0 | 10727 | 43.5 | 0.4164 | 78038 | 26.7 | 10.812720 |
| 342 | Macomb County | MI | 05000US26099 | -82.910869 | 42.671467 | 867730 | 706518.0 | 100689.0 | 32772.0 | 1762.0 | 50.0 | 4781.0 | 15331.0 | 37462.0 | 189808.0 | 208460.0 | 151316.0 | 93270 | 41.0 | 0.4290 | 60143 | 29.4 | 12.693460 |
| 343 | Marquette County | MI | 05000US26103 | -87.584028 | 46.656596 | 66435 | 61683.0 | 808.0 | 649.0 | 207.0 | 15.0 | 283.0 | 413.0 | 1300.0 | 13094.0 | 14036.0 | 14640.0 | 10108 | 38.4 | 0.4318 | 51275 | 32.7 | 17.229494 |
| 344 | Midland County | MI | 05000US26111 | -84.379219 | 43.648378 | 83462 | 77534.0 | 1235.0 | 1738.0 | 228.0 | 0.0 | 403.0 | 675.0 | 2446.0 | 16334.0 | 19114.0 | 19756.0 | 6711 | 42.6 | 0.4636 | 57269 | 27.2 | 15.566737 |
| 345 | Monroe County | MI | 05000US26115 | -83.487106 | 41.916097 | 149208 | 140808.0 | 4242.0 | 952.0 | 804.0 | 0.0 | 514.0 | 1152.0 | 7040.0 | 37166.0 | 37585.0 | 20139.0 | 14320 | 42.2 | 0.4146 | 60799 | 28.4 | 14.140400 |
| 346 | Muskegon County | MI | 05000US26121 | -86.751892 | 43.289258 | 173408 | 139261.0 | 24450.0 | 1128.0 | 1249.0 | 0.0 | 1092.0 | 1406.0 | 9256.0 | 41069.0 | 44723.0 | 19575.0 | 31910 | 39.1 | 0.4316 | 44264 | 29.8 | 18.908263 |
| 347 | Oakland County | MI | 05000US26125 | -83.384210 | 42.660452 | 1243970 | 934642.0 | 167411.0 | 87646.0 | 3016.0 | 232.0 | 11061.0 | 11112.0 | 35856.0 | 161799.0 | 255592.0 | 404076.0 | 104971 | 41.0 | 0.4757 | 71920 | 26.4 | 9.636146 |
| 348 | Ottawa County | MI | 05000US26139 | -86.655342 | 42.942346 | 282250 | 255093.0 | 3439.0 | 6244.0 | 628.0 | 165.0 | 7174.0 | 3754.0 | 7787.0 | 53335.0 | 52659.0 | 55773.0 | 25957 | 35.0 | 0.4069 | 64513 | 26.5 | 10.278317 |
| 349 | Saginaw County | MI | 05000US26145 | -84.055410 | 43.328267 | 192326 | 144965.0 | 35768.0 | 2503.0 | 493.0 | 61.0 | 2800.0 | 1332.0 | 10133.0 | 43655.0 | 44580.0 | 29891.0 | 32331 | 41.0 | 0.4522 | 45849 | 31.1 | 19.415497 |
| 350 | St. Clair County | MI | 05000US26147 | -82.668914 | 42.928804 | 159587 | 148831.0 | 3835.0 | 1223.0 | 358.0 | 0.0 | 1247.0 | 1675.0 | 9658.0 | 39983.0 | 41221.0 | 18456.0 | 21899 | 43.6 | 0.4293 | 51864 | 29.7 | 16.452246 |
| 351 | Shiawassee County | MI | 05000US26155 | -84.146352 | 42.951545 | 68554 | NaN | NaN | NaN | NaN | NaN | NaN | 176.0 | 2392.0 | 17896.0 | 19573.0 | 7739.0 | 7392 | 41.8 | 0.4251 | 53244 | 27.8 | 17.794165 |
| 352 | Van Buren County | MI | 05000US26159 | -86.305696 | 42.283986 | 75223 | 64948.0 | 2312.0 | 425.0 | 1240.0 | 146.0 | 2211.0 | 1265.0 | 4310.0 | 17068.0 | 17780.0 | 10307.0 | 12872 | 40.7 | 0.4770 | 47917 | 27.3 | 22.803968 |
| 353 | Washtenaw County | MI | 05000US26161 | -83.844634 | 42.252327 | 364709 | 269158.0 | 43375.0 | 31232.0 | 1789.0 | 179.0 | 2218.0 | 1794.0 | 7360.0 | 33885.0 | 58372.0 | 124255.0 | 50914 | 33.5 | 0.4903 | 65601 | 30.7 | 8.351716 |
| 354 | Wayne County | MI | 05000US26163 | -83.261953 | 42.284664 | 1749366 | 924131.0 | 680630.0 | 54230.0 | 5458.0 | 420.0 | 36368.0 | 32912.0 | 115053.0 | 350222.0 | 384204.0 | 269403.0 | 391366 | 37.8 | 0.5010 | 43464 | 33.0 | 20.638415 |
| 355 | Anoka County | MN | 05000US27003 | -93.242723 | 45.274110 | 345957 | 291760.0 | 19779.0 | 15529.0 | 2480.0 | 195.0 | 5151.0 | 3481.0 | 9536.0 | 68923.0 | 87629.0 | 63583.0 | 22871 | 38.1 | 0.3960 | 76533 | 29.7 | 10.029492 |
| 356 | Blue Earth County | MN | 05000US27013 | -94.064071 | 44.038225 | 66441 | NaN | NaN | NaN | NaN | NaN | NaN | 387.0 | 1480.0 | 11077.0 | 12410.0 | 12501.0 | 10188 | 30.5 | 0.4353 | 53869 | 29.7 | 11.764706 |
| 357 | Carver County | MN | 05000US27019 | -93.800575 | 44.821381 | 100262 | NaN | NaN | NaN | NaN | NaN | NaN | 207.0 | 1206.0 | 11950.0 | 20706.0 | 31531.0 | 4038 | 38.0 | 0.4314 | 92455 | 24.1 | 7.285776 |
| 358 | Dakota County | MN | 05000US27037 | -93.062481 | 44.670893 | 417486 | 345463.0 | 24024.0 | 20140.0 | 1195.0 | 102.0 | 12277.0 | 4372.0 | 8901.0 | 55783.0 | 94348.0 | 115247.0 | 22262 | 37.6 | 0.4120 | 78662 | 28.0 | 8.183895 |
| 359 | Hennepin County | MN | 05000US27053 | -93.475185 | 45.006064 | 1232483 | 893871.0 | 158741.0 | 85906.0 | 9923.0 | 483.0 | 40359.0 | 20435.0 | 33098.0 | 141239.0 | 235715.0 | 415202.0 | 131859 | 36.3 | 0.4862 | 71200 | 28.3 | 11.148446 |
| 360 | Olmsted County | MN | 05000US27109 | -92.406722 | 44.003429 | 153102 | 129033.0 | 9005.0 | 8964.0 | 900.0 | 0.0 | 2226.0 | 1415.0 | 3325.0 | 20124.0 | 32869.0 | 43958.0 | 13844 | 37.4 | 0.4430 | 72428 | 30.2 | 10.399791 |
| 361 | Ramsey County | MN | 05000US27123 | -93.100141 | 45.015250 | 540649 | 358606.0 | 59737.0 | 76470.0 | 3089.0 | 209.0 | 15811.0 | 15252.0 | 15834.0 | 80089.0 | 96992.0 | 146659.0 | 72994 | 34.9 | 0.4610 | 60369 | 28.8 | 11.956808 |
| 362 | Rice County | MN | 05000US27131 | -93.298503 | 44.350943 | 65622 | 56157.0 | 3451.0 | 1404.0 | 86.0 | 0.0 | 2872.0 | 1351.0 | 3145.0 | 12397.0 | 13548.0 | 11417.0 | 5285 | 37.6 | 0.4123 | 69135 | 29.2 | 15.514304 |
| 363 | St. Louis County | MN | 05000US27137 | -92.463645 | 47.583852 | 199980 | 184278.0 | 3353.0 | 2228.0 | 4859.0 | 45.0 | 938.0 | 1021.0 | 6454.0 | 38594.0 | 51528.0 | 37201.0 | 29101 | 41.6 | 0.4483 | 49825 | 30.1 | 18.099096 |
| 364 | Scott County | MN | 05000US27139 | -93.534553 | 44.651932 | 143680 | 119736.0 | 4743.0 | 8060.0 | 681.0 | 0.0 | 5193.0 | 1861.0 | 1249.0 | 21944.0 | 29687.0 | 37087.0 | 9387 | 36.7 | 0.3974 | 88765 | 32.6 | 6.820490 |
| 365 | Sherburne County | MN | 05000US27141 | -93.775092 | 45.443171 | 93528 | 86674.0 | 2417.0 | 791.0 | 272.0 | 0.0 | 1213.0 | 326.0 | 2003.0 | 16808.0 | 24757.0 | 15564.0 | 5986 | 35.8 | 0.3586 | 83675 | 27.6 | 8.934566 |
| 366 | Stearns County | MN | 05000US27145 | -94.610482 | 45.555234 | 155652 | 138022.0 | 8669.0 | 3701.0 | 222.0 | 0.0 | 2483.0 | 1268.0 | 3548.0 | 27695.0 | 37060.0 | 24970.0 | 18223 | 34.4 | 0.4289 | 57728 | 25.3 | 10.728655 |
| 367 | Washington County | MN | 05000US27163 | -92.890117 | 45.037929 | 253117 | 216807.0 | 11366.0 | 12637.0 | 1002.0 | 66.0 | 3014.0 | 2150.0 | 4238.0 | 38142.0 | 53098.0 | 70527.0 | 10615 | 39.1 | 0.3968 | 90256 | 27.4 | 7.696138 |
| 368 | Wright County | MN | 05000US27171 | -93.966397 | 45.175091 | 132550 | NaN | NaN | NaN | NaN | NaN | NaN | 1365.0 | 3111.0 | 22844.0 | 31160.0 | 26202.0 | 5952 | 36.6 | 0.3934 | 74190 | 28.7 | 10.514809 |
| 369 | Boone County | MO | 05000US29019 | -92.310779 | 38.989657 | 176594 | 142618.0 | 16256.0 | 9021.0 | 811.0 | 23.0 | 2821.0 | 1211.0 | 3543.0 | 22503.0 | 31306.0 | 46454.0 | 29321 | 30.6 | 0.4706 | 52752 | 28.6 | 9.862305 |
| 370 | Buchanan County | MO | 05000US29021 | -94.808173 | 39.660370 | 88938 | 77933.0 | 4572.0 | 1054.0 | 372.0 | 0.0 | 1607.0 | 1819.0 | 4773.0 | 20444.0 | 18539.0 | 13661.0 | 16133 | 37.9 | 0.4715 | 48550 | 26.7 | 22.554532 |
| 371 | Cape Girardeau County | MO | 05000US29031 | -89.684908 | 37.383882 | 78913 | NaN | NaN | NaN | NaN | NaN | NaN | 456.0 | 3319.0 | 15670.0 | 15317.0 | 15545.0 | 12284 | 36.0 | 0.4691 | 50223 | 27.0 | 12.513613 |
| 372 | Cass County | MO | 05000US29037 | -94.354242 | 38.647159 | 102845 | 93928.0 | 3018.0 | 802.0 | 129.0 | 0.0 | 917.0 | 761.0 | 3092.0 | 23397.0 | 24977.0 | 16813.0 | 9171 | 40.0 | 0.3959 | 63420 | 30.4 | 14.621758 |
| 373 | Christian County | MO | 05000US29043 | -93.187614 | 36.969739 | 84401 | NaN | NaN | NaN | NaN | NaN | NaN | 303.0 | 4443.0 | 15560.0 | 20877.0 | 14562.0 | 10091 | 37.3 | 0.4098 | 53172 | 30.2 | 11.891979 |
| 374 | Clay County | MO | 05000US29047 | -94.421502 | 39.315551 | 239085 | 204294.0 | 15923.0 | 6193.0 | 534.0 | 1184.0 | 4577.0 | 1492.0 | 6509.0 | 47246.0 | 49776.0 | 54652.0 | 20861 | 37.4 | 0.4087 | 65430 | 25.7 | 7.469966 |
| 375 | Cole County | MO | 05000US29051 | -92.271404 | 38.506847 | 76631 | 64560.0 | 8477.0 | 1107.0 | 290.0 | 39.0 | 254.0 | 537.0 | 2937.0 | 15435.0 | 14723.0 | 17376.0 | 7459 | 39.2 | 0.4010 | 55303 | 21.7 | 20.680113 |
| 376 | Franklin County | MO | 05000US29071 | -91.073410 | 38.408313 | 102838 | NaN | NaN | NaN | NaN | NaN | NaN | 1151.0 | 4911.0 | 22643.0 | 24319.0 | 16526.0 | 11170 | 40.9 | 0.4325 | 55359 | 24.1 | 16.358094 |
| 377 | Greene County | MO | 05000US29077 | -93.340641 | 37.258196 | 288690 | 258094.0 | 10029.0 | 5704.0 | 2188.0 | 110.0 | 4975.0 | 2276.0 | 10013.0 | 54204.0 | 62586.0 | 55713.0 | 46951 | 36.0 | 0.4718 | 42062 | 29.0 | 14.022377 |
| 378 | Jackson County | MO | 05000US29095 | -94.342507 | 39.007233 | 691801 | 463423.0 | 161932.0 | 12585.0 | 3089.0 | 2433.0 | 25949.0 | 9918.0 | 27962.0 | 138405.0 | 141523.0 | 145605.0 | 106061 | 36.5 | 0.4690 | 50815 | 28.2 | 15.905076 |
| 379 | Jasper County | MO | 05000US29097 | -94.338869 | 37.200865 | 119111 | 107566.0 | 2615.0 | 1634.0 | 2263.0 | 139.0 | 2123.0 | 2421.0 | 6841.0 | 28050.0 | 23002.0 | 16349.0 | 21358 | 36.3 | 0.4517 | 45786 | 27.8 | 15.287557 |
| 380 | Jefferson County | MO | 05000US29099 | -90.543138 | 38.257414 | 224226 | 215622.0 | 2229.0 | 1882.0 | 580.0 | 0.0 | 365.0 | 1621.0 | 15788.0 | 48600.0 | 57655.0 | 28690.0 | 22710 | 39.3 | 0.3806 | 61559 | 27.4 | 12.393438 |
| 381 | Platte County | MO | 05000US29165 | -94.761472 | 39.378696 | 98309 | 84433.0 | 6635.0 | 2838.0 | 314.0 | 73.0 | 998.0 | 451.0 | 2164.0 | 13771.0 | 20969.0 | 28393.0 | 5435 | 38.3 | 0.4288 | 77581 | 25.7 | 8.666192 |
| 382 | St. Charles County | MO | 05000US29183 | -90.674915 | 38.781102 | 390918 | 352403.0 | 17540.0 | 9844.0 | 500.0 | 0.0 | 1809.0 | 1160.0 | 9518.0 | 63575.0 | 83715.0 | 104005.0 | 18592 | 38.0 | 0.3873 | 80644 | 24.7 | 9.147619 |
| 383 | St. Francois County | MO | 05000US29187 | -90.473868 | 37.810707 | 66627 | NaN | NaN | NaN | NaN | NaN | NaN | 470.0 | 5062.0 | 18697.0 | 14932.0 | 6312.0 | 7296 | 39.7 | 0.4268 | 45431 | 29.4 | 20.365503 |
| 384 | St. Louis County | MO | 05000US29189 | -90.445954 | 38.640702 | 998581 | 684030.0 | 238612.0 | 39819.0 | 1834.0 | 271.0 | 8883.0 | 7747.0 | 31886.0 | 146304.0 | 196490.0 | 300433.0 | 87235 | 40.3 | 0.4859 | 62572 | 27.4 | 11.332578 |
| 385 | St. Louis city | MO | 05000US29510 | -90.244582 | 38.635699 | 311404 | 144752.0 | 145886.0 | 10264.0 | 762.0 | 88.0 | 3317.0 | 3688.0 | 21968.0 | 52086.0 | 63719.0 | 74608.0 | 72043 | 35.3 | 0.5310 | 40346 | 29.0 | 22.111767 |
| 386 | DeSoto County | MS | 05000US28033 | -89.993240 | 34.874266 | 175611 | 119958.0 | 46205.0 | 2663.0 | 416.0 | 0.0 | 3501.0 | 1914.0 | 7403.0 | 33075.0 | 42034.0 | 28670.0 | 18199 | 37.2 | 0.4243 | 64505 | 26.7 | 11.912056 |
| 387 | Forrest County | MS | 05000US28035 | -89.259447 | 31.188580 | 75979 | NaN | NaN | NaN | NaN | NaN | NaN | 815.0 | 3437.0 | 13358.0 | 16853.0 | 11700.0 | 19704 | 31.9 | 0.5268 | 37275 | 30.7 | 23.572678 |
| 388 | Harrison County | MS | 05000US28047 | -89.083376 | 30.416536 | 203234 | 139366.0 | 49451.0 | 5702.0 | 1225.0 | 0.0 | 1604.0 | 2730.0 | 11987.0 | 39605.0 | 49857.0 | 26982.0 | 40990 | 35.6 | 0.4526 | 42770 | 31.3 | 20.276087 |
| 389 | Hinds County | MS | 05000US28049 | -90.465900 | 32.267924 | 241229 | NaN | NaN | NaN | NaN | NaN | NaN | 3492.0 | 12602.0 | 38521.0 | 51710.0 | 45957.0 | 45159 | 34.4 | 0.4799 | 43657 | 31.0 | 18.013507 |
| 390 | Jackson County | MS | 05000US28059 | -88.619991 | 30.458491 | 141241 | 98840.0 | 31706.0 | 3863.0 | 506.0 | 0.0 | 4189.0 | 2269.0 | 6478.0 | 30424.0 | 32144.0 | 21252.0 | 25397 | 38.0 | 0.4463 | 54606 | 28.5 | 21.004319 |
| 391 | Jones County | MS | 05000US28067 | -89.167262 | 31.621044 | 67953 | NaN | NaN | NaN | NaN | NaN | NaN | 1776.0 | 4133.0 | 12456.0 | 16464.0 | 8425.0 | 13370 | 36.8 | 0.4729 | 43350 | 35.7 | 39.018513 |
| 392 | Lauderdale County | MS | 05000US28075 | -88.660449 | 32.403998 | 77755 | NaN | NaN | NaN | NaN | NaN | NaN | 1504.0 | 4431.0 | 15711.0 | 17630.0 | 11135.0 | 17469 | 37.8 | 0.4672 | 42364 | 28.0 | 26.840496 |
| 393 | Lee County | MS | 05000US28081 | -88.680887 | 34.288964 | 85381 | NaN | NaN | NaN | NaN | NaN | NaN | 1074.0 | 7436.0 | 14652.0 | 19262.0 | 13538.0 | 14675 | 38.3 | 0.4626 | 42235 | 32.8 | 23.704417 |
| 394 | Madison County | MS | 05000US28089 | -90.034160 | 32.634370 | 105114 | NaN | NaN | NaN | NaN | NaN | NaN | 649.0 | 4623.0 | 11152.0 | 19539.0 | 31861.0 | 12644 | 37.6 | 0.4774 | 64350 | 31.8 | 16.616559 |
| 395 | Rankin County | MS | 05000US28121 | -89.946552 | 32.262057 | 150228 | NaN | NaN | NaN | NaN | NaN | NaN | 2165.0 | 7755.0 | 30290.0 | 30891.0 | 29790.0 | 10752 | 37.1 | 0.3843 | 62332 | 22.6 | 14.897166 |
| 396 | Cascade County | MT | 05000US30013 | -111.350571 | 47.316443 | 81755 | 72572.0 | 992.0 | 738.0 | 3673.0 | 142.0 | 325.0 | 548.0 | 3317.0 | 16572.0 | 21078.0 | 12943.0 | 11861 | 38.6 | 0.4703 | 45138 | 29.1 | 20.610800 |
| 397 | Flathead County | MT | 05000US30029 | -114.054319 | 48.314696 | 98082 | 92689.0 | 282.0 | 340.0 | 848.0 | 163.0 | 679.0 | 166.0 | 3542.0 | 19471.0 | 24851.0 | 21006.0 | 11585 | 42.3 | 0.4696 | 50142 | 34.9 | 12.737086 |
| 398 | Gallatin County | MT | 05000US30031 | -111.173443 | 45.535559 | 104502 | NaN | NaN | NaN | NaN | NaN | NaN | 492.0 | 1816.0 | 11272.0 | 19703.0 | 32812.0 | 13242 | 33.7 | 0.5056 | 61211 | 29.1 | 11.542730 |
| 399 | Lewis and Clark County | MT | 05000US30049 | -112.382954 | 47.122133 | 67282 | NaN | NaN | NaN | NaN | NaN | NaN | 43.0 | 1231.0 | 11718.0 | 16146.0 | 17726.0 | 6753 | 41.6 | 0.4590 | 63475 | 25.4 | 11.919510 |
| 400 | Missoula County | MT | 05000US30063 | -113.892681 | 47.027263 | 116130 | 106680.0 | 715.0 | 1855.0 | 3019.0 | 0.0 | 237.0 | 434.0 | 4447.0 | 15886.0 | 24861.0 | 32019.0 | 18709 | 36.4 | 0.4981 | 46550 | 30.0 | 12.804610 |
| 401 | Yellowstone County | MT | 05000US30111 | -108.276656 | 45.936987 | 158437 | 144305.0 | 829.0 | 959.0 | 6176.0 | 0.0 | 786.0 | 788.0 | 4647.0 | 35340.0 | 31265.0 | 34353.0 | 11353 | 38.9 | 0.4259 | 57945 | 26.0 | 13.111552 |
| 402 | Alamance County | NC | 05000US37001 | -79.399935 | 36.041974 | 159688 | 110933.0 | 31088.0 | 2245.0 | 779.0 | 307.0 | 11140.0 | 3756.0 | 9772.0 | 27697.0 | 41452.0 | 24330.0 | 25864 | 39.9 | 0.4336 | 45100 | 27.4 | 18.193540 |
| 403 | Brunswick County | NC | 05000US37019 | -78.227688 | 34.038708 | 126953 | NaN | NaN | NaN | NaN | NaN | NaN | 610.0 | 6425.0 | 29090.0 | 35151.0 | 26984.0 | 18025 | 53.4 | 0.4748 | 50692 | 41.7 | 15.311701 |
| 404 | Buncombe County | NC | 05000US37021 | -82.530426 | 35.609371 | 256088 | 226953.0 | 15445.0 | 3542.0 | 959.0 | 425.0 | 3181.0 | 2741.0 | 12604.0 | 44306.0 | 54871.0 | 70529.0 | 32645 | 42.6 | 0.4630 | 50685 | 28.8 | 16.004538 |
| 405 | Burke County | NC | 05000US37023 | -81.706180 | 35.746182 | 88851 | NaN | NaN | NaN | NaN | NaN | NaN | 2153.0 | 7015.0 | 20344.0 | 21355.0 | 10414.0 | 16523 | 45.0 | 0.4493 | 40345 | 26.8 | 26.568402 |
| 406 | Cabarrus County | NC | 05000US37025 | -80.552868 | 35.387845 | 201590 | 141332.0 | 34402.0 | 6212.0 | 511.0 | 374.0 | 12541.0 | 3429.0 | 8755.0 | 30160.0 | 45635.0 | 42254.0 | 23557 | 37.7 | 0.4151 | 63386 | 24.9 | 8.766575 |
| 407 | Caldwell County | NC | 05000US37027 | -81.530076 | 35.957857 | 81449 | NaN | NaN | NaN | NaN | NaN | NaN | 1233.0 | 9060.0 | 19544.0 | 18501.0 | 7932.0 | 14100 | 43.3 | 0.4547 | 36301 | 30.7 | 22.354228 |
| 408 | Carteret County | NC | 05000US37031 | -76.526967 | 34.858313 | 68890 | 61844.0 | 3798.0 | 546.0 | 285.0 | 0.0 | 320.0 | 1409.0 | 2103.0 | 15026.0 | 19238.0 | 13237.0 | 7411 | 48.0 | 0.4404 | 51206 | 26.1 | 16.589108 |
| 409 | Catawba County | NC | 05000US37035 | -81.214151 | 35.663182 | 156459 | 122004.0 | 13875.0 | 6466.0 | 600.0 | 0.0 | 10980.0 | 2957.0 | 10828.0 | 33011.0 | 37069.0 | 22744.0 | 18490 | 41.3 | 0.4415 | 48913 | 25.7 | 16.227401 |
| 410 | Chatham County | NC | 05000US37037 | -79.251454 | 35.704994 | 72243 | 59488.0 | 8033.0 | 868.0 | 0.0 | 0.0 | 1187.0 | 2335.0 | 3461.0 | 10641.0 | 13178.0 | 22522.0 | 9732 | 47.1 | 0.5104 | 62961 | 31.7 | 13.728647 |
| 411 | Cleveland County | NC | 05000US37045 | -81.557114 | 35.334630 | 97144 | NaN | NaN | NaN | NaN | NaN | NaN | 1496.0 | 7636.0 | 19771.0 | 22150.0 | 13084.0 | 20671 | 40.7 | 0.4851 | 36992 | 29.9 | 29.193792 |
| 412 | Craven County | NC | 05000US37049 | -77.082541 | 35.118179 | 103445 | 71552.0 | 20612.0 | 3215.0 | 812.0 | 50.0 | 3030.0 | 1861.0 | 5461.0 | 16131.0 | 24892.0 | 17874.0 | 15114 | 37.4 | 0.4605 | 49938 | 29.0 | 14.601583 |
| 413 | Cumberland County | NC | 05000US37051 | -78.828719 | 35.050192 | 327127 | 162910.0 | 124566.0 | 7649.0 | 5012.0 | 737.0 | 7461.0 | 3647.0 | 12547.0 | 52864.0 | 80345.0 | 51833.0 | 59784 | 32.0 | 0.4403 | 45205 | 29.9 | 14.531424 |
| 414 | Davidson County | NC | 05000US37057 | -80.206525 | 35.795122 | 164926 | 139762.0 | 13621.0 | 2630.0 | 133.0 | 0.0 | 4359.0 | 3117.0 | 12625.0 | 38479.0 | 37457.0 | 19956.0 | 25583 | 42.3 | 0.4338 | 45678 | 25.9 | 17.175213 |
| 415 | Durham County | NC | 05000US37063 | -78.877919 | 36.036589 | 306212 | 158099.0 | 113137.0 | 15714.0 | 669.0 | 267.0 | 10281.0 | 7603.0 | 15300.0 | 37252.0 | 46339.0 | 100302.0 | 47869 | 35.1 | 0.4808 | 53832 | 30.0 | 12.566190 |
| 416 | Forsyth County | NC | 05000US37067 | -80.257289 | 36.131667 | 371511 | 245900.0 | 97584.0 | 8532.0 | 1513.0 | 21.0 | 8879.0 | 7991.0 | 15271.0 | 61050.0 | 75172.0 | 85056.0 | 65062 | 38.0 | 0.5159 | 48271 | 30.0 | 16.663361 |
| 417 | Gaston County | NC | 05000US37071 | -81.177256 | 35.293344 | 216965 | 167912.0 | 34358.0 | 3627.0 | 1004.0 | 0.0 | 5169.0 | 3646.0 | 16966.0 | 44953.0 | 48133.0 | 32406.0 | 33374 | 39.8 | 0.4933 | 48711 | 27.9 | 16.345714 |
| 418 | Guilford County | NC | 05000US37081 | -79.788665 | 36.079065 | 521330 | 290962.0 | 175229.0 | 26138.0 | 1975.0 | 383.0 | 14980.0 | 10780.0 | 24124.0 | 85861.0 | 99670.0 | 119884.0 | 93700 | 37.1 | 0.4984 | 47262 | 30.0 | 16.580985 |
| 419 | Harnett County | NC | 05000US37085 | -78.871610 | 35.368635 | 130881 | 86920.0 | 27503.0 | 916.0 | 878.0 | 202.0 | 8723.0 | 2757.0 | 6929.0 | 24954.0 | 31725.0 | 16735.0 | 20298 | 34.0 | 0.4151 | 51637 | 28.8 | 17.834395 |
| 420 | Henderson County | NC | 05000US37089 | -82.479634 | 35.336424 | 114209 | NaN | NaN | NaN | NaN | NaN | NaN | 2799.0 | 5978.0 | 22277.0 | 27022.0 | 25949.0 | 12990 | 47.3 | 0.4303 | 54963 | 27.3 | 15.071875 |
| 421 | Iredell County | NC | 05000US37097 | -80.874545 | 35.806356 | 172916 | 142713.0 | 21633.0 | 4410.0 | 194.0 | 0.0 | 1728.0 | 2774.0 | 8661.0 | 34785.0 | 37520.0 | 31022.0 | 18051 | 40.8 | 0.4764 | 55891 | 24.7 | 15.008380 |
| 422 | Johnston County | NC | 05000US37101 | -78.367267 | 35.513405 | 191450 | 149935.0 | 31782.0 | 536.0 | 729.0 | 0.0 | 3042.0 | 3825.0 | 11455.0 | 33917.0 | 43908.0 | 30060.0 | 24672 | 38.0 | 0.3930 | 54719 | 28.0 | 19.650134 |
| 423 | Lincoln County | NC | 05000US37109 | -81.225176 | 35.487825 | 81168 | NaN | NaN | NaN | NaN | NaN | NaN | 910.0 | 4835.0 | 18667.0 | 20540.0 | 12202.0 | 10797 | 44.3 | 0.4423 | 49453 | 28.3 | 16.962138 |
| 424 | Mecklenburg County | NC | 05000US37119 | -80.833832 | 35.246862 | 1054835 | 574931.0 | 334621.0 | 59544.0 | 2740.0 | 786.0 | 55771.0 | 20475.0 | 37063.0 | 121213.0 | 202835.0 | 313875.0 | 126156 | 35.0 | 0.4780 | 62978 | 28.0 | 10.661971 |
| 425 | Moore County | NC | 05000US37125 | -79.480664 | 35.310163 | 95776 | NaN | NaN | NaN | NaN | NaN | NaN | 1358.0 | 3043.0 | 15082.0 | 22965.0 | 26150.0 | 9657 | 45.4 | 0.4585 | 52279 | 24.0 | 15.644820 |
| 426 | Nash County | NC | 05000US37127 | -77.987555 | 35.965945 | 94005 | 50212.0 | 37748.0 | 845.0 | 434.0 | 0.0 | 3161.0 | 1703.0 | 7781.0 | 22527.0 | 19068.0 | 13445.0 | 14090 | 41.8 | 0.4855 | 47902 | 26.0 | 22.994154 |
| 427 | New Hanover County | NC | 05000US37129 | -77.871378 | 34.177466 | 223483 | 180970.0 | 31080.0 | 2161.0 | 625.0 | 0.0 | 2255.0 | 2160.0 | 9258.0 | 35436.0 | 45587.0 | 59649.0 | 40337 | 39.1 | 0.5125 | 50028 | 32.6 | 13.455737 |
| 428 | Onslow County | NC | 05000US37133 | -77.503297 | 34.763460 | 187136 | 139353.0 | 24311.0 | 3350.0 | 591.0 | 413.0 | 3224.0 | 1421.0 | 7133.0 | 29459.0 | 43180.0 | 18705.0 | 22662 | 26.6 | 0.3787 | 47150 | 27.8 | 11.673089 |
| 429 | Orange County | NC | 05000US37135 | -79.119355 | 36.062499 | 141796 | 106590.0 | 14958.0 | 11270.0 | 677.0 | 0.0 | 1976.0 | 2677.0 | 3779.0 | 11272.0 | 18149.0 | 50931.0 | 16820 | 34.1 | 0.4949 | 66423 | 31.8 | 8.903850 |
| 430 | Pitt County | NC | 05000US37147 | -77.372404 | 35.591065 | 177220 | 102197.0 | 62515.0 | 2983.0 | 640.0 | 64.0 | 4316.0 | 2159.0 | 7293.0 | 24107.0 | 38080.0 | 33640.0 | 36936 | 32.1 | 0.4907 | 46573 | 31.8 | 14.657450 |
| 431 | Randolph County | NC | 05000US37151 | -79.806215 | 35.709915 | 143416 | 119977.0 | 9771.0 | 1463.0 | 262.0 | 0.0 | 9133.0 | 3613.0 | 11962.0 | 32028.0 | 32344.0 | 16173.0 | 20046 | 42.6 | 0.4036 | 44836 | 27.6 | 21.779092 |
| 432 | Robeson County | NC | 05000US37155 | -79.100881 | 34.639210 | 133235 | 38442.0 | 32385.0 | 958.0 | 52789.0 | 197.0 | 6244.0 | 4174.0 | 12590.0 | 30024.0 | 25345.0 | 11328.0 | 35298 | 36.3 | 0.4844 | 34254 | 26.4 | 43.097755 |
| 433 | Rockingham County | NC | 05000US37157 | -79.782889 | 36.380927 | 91393 | NaN | NaN | NaN | NaN | NaN | NaN | 1690.0 | 9507.0 | 21975.0 | 20959.0 | 9321.0 | 16752 | 43.7 | 0.4349 | 40985 | 26.3 | 27.293452 |
| 434 | Rowan County | NC | 05000US37159 | -80.525344 | 35.639218 | 139933 | 107453.0 | 22460.0 | 1104.0 | 659.0 | 0.0 | 5519.0 | 2114.0 | 8495.0 | 30628.0 | 33023.0 | 18944.0 | 22367 | 39.4 | 0.4447 | 48379 | 24.9 | 19.899079 |
| 435 | Rutherford County | NC | 05000US37161 | -81.919582 | 35.402747 | 66421 | NaN | NaN | NaN | NaN | NaN | NaN | 1920.0 | 5286.0 | 15631.0 | 14798.0 | 8617.0 | 11156 | 45.6 | 0.4511 | 36121 | 25.5 | 26.643688 |
| 436 | Surry County | NC | 05000US37171 | -80.686463 | 36.415416 | 72113 | NaN | NaN | NaN | NaN | NaN | NaN | 2753.0 | 6387.0 | 15174.0 | 16067.0 | 7635.0 | 11826 | 44.3 | 0.5322 | 36802 | 31.1 | 23.661583 |
| 437 | Union County | NC | 05000US37179 | -80.530131 | 34.991501 | 226606 | 187188.0 | 25691.0 | 5792.0 | 528.0 | 0.0 | 2272.0 | 4014.0 | 12280.0 | 35366.0 | 42185.0 | 48761.0 | 20041 | 38.4 | 0.4739 | 71588 | 29.4 | 8.175167 |
| 438 | Wake County | NC | 05000US37183 | -78.650624 | 35.789846 | 1046791 | 688226.0 | 213306.0 | 69541.0 | 3815.0 | 157.0 | 40616.0 | 14972.0 | 26186.0 | 98411.0 | 177501.0 | 368828.0 | 93252 | 36.0 | 0.4458 | 76097 | 26.7 | 6.130938 |
| 439 | Wayne County | NC | 05000US37191 | -78.004826 | 35.362741 | 124150 | 79743.0 | 37003.0 | 1539.0 | 324.0 | 65.0 | 1082.0 | 4286.0 | 7922.0 | 24852.0 | 29484.0 | 14509.0 | 26588 | 37.3 | 0.4755 | 41711 | 30.3 | 21.850127 |
| 440 | Wilkes County | NC | 05000US37193 | -81.165354 | 36.209303 | 68740 | NaN | NaN | NaN | NaN | NaN | NaN | 1254.0 | 5568.0 | 16286.0 | 16635.0 | 7407.0 | 10424 | 44.2 | 0.4259 | 41332 | 29.0 | 22.512252 |
| 441 | Wilson County | NC | 05000US37195 | -77.918982 | 35.704125 | 81661 | NaN | NaN | NaN | NaN | NaN | NaN | 3182.0 | 6631.0 | 18832.0 | 14808.0 | 10220.0 | 18167 | 40.3 | 0.4998 | 39036 | 32.0 | 24.895621 |
| 442 | Burleigh County | ND | 05000US38015 | -100.462001 | 46.971843 | 94487 | NaN | NaN | NaN | NaN | NaN | NaN | 291.0 | 3421.0 | 14493.0 | 22746.0 | 21090.0 | 6523 | 36.5 | 0.4396 | 64558 | 23.0 | 13.417786 |
| 443 | Cass County | ND | 05000US38017 | -97.252375 | 46.927003 | 175249 | 153678.0 | 9281.0 | 4496.0 | 1478.0 | 0.0 | 908.0 | 1204.0 | 3828.0 | 20677.0 | 38033.0 | 45102.0 | 19764 | 32.4 | 0.4404 | 59240 | 25.6 | 9.316852 |
| 444 | Grand Forks County | ND | 05000US38035 | -97.450851 | 47.926003 | 71083 | NaN | NaN | NaN | NaN | NaN | NaN | 167.0 | 1735.0 | 11376.0 | 14646.0 | 13996.0 | 10730 | 29.9 | 0.4599 | 50008 | 30.3 | 12.790814 |
| 445 | Ward County | ND | 05000US38101 | -101.540537 | 48.216686 | 70210 | 60834.0 | 1578.0 | 865.0 | 3000.0 | 0.0 | 1717.0 | 175.0 | 2062.0 | 14349.0 | 14356.0 | 12236.0 | 5647 | 30.7 | 0.4151 | 62573 | 29.2 | 6.682732 |
| 446 | Douglas County | NE | 05000US31055 | -96.154066 | 41.297091 | 554995 | 444192.0 | 61497.0 | 19640.0 | 1527.0 | 390.0 | 11142.0 | 14833.0 | 18563.0 | 79874.0 | 105217.0 | 137940.0 | 67359 | 34.5 | 0.4654 | 58990 | 29.4 | 13.705605 |
| 447 | Lancaster County | NE | 05000US31109 | -96.688658 | 40.783547 | 309637 | 267852.0 | 12445.0 | 12252.0 | 1952.0 | 3.0 | 4680.0 | 3717.0 | 7766.0 | 40628.0 | 60673.0 | 77214.0 | 37323 | 33.1 | 0.4567 | 58845 | 28.5 | 10.098585 |
| 448 | Sarpy County | NE | 05000US31153 | -96.109125 | 41.115063 | 179023 | 158913.0 | 6658.0 | 4776.0 | 595.0 | 168.0 | 2121.0 | 1719.0 | 3654.0 | 25466.0 | 37697.0 | 44947.0 | 10743 | 34.7 | 0.3843 | 73569 | 26.6 | 9.331977 |
| 449 | Cheshire County | NH | 05000US33005 | -72.248217 | 42.925455 | 75774 | 72373.0 | 520.0 | 1122.0 | 148.0 | 0.0 | 197.0 | 682.0 | 2954.0 | 17187.0 | 13961.0 | 17451.0 | 4994 | 42.9 | 0.4382 | 56364 | 30.8 | 14.591545 |
| 450 | Grafton County | NH | 05000US33009 | -71.842264 | 43.926488 | 88888 | NaN | NaN | NaN | NaN | NaN | NaN | 320.0 | 3157.0 | 16606.0 | 14423.0 | 27601.0 | 9008 | 43.1 | 0.5077 | 61520 | 29.0 | 13.825791 |
| 451 | Hillsborough County | NH | 05000US33011 | -71.723101 | 42.911643 | 407761 | 367574.0 | 9702.0 | 14874.0 | 467.0 | 32.0 | 5283.0 | 3561.0 | 16251.0 | 72925.0 | 82398.0 | 106696.0 | 32563 | 40.4 | 0.4186 | 76254 | 27.8 | 9.200955 |
| 452 | Merrimack County | NH | 05000US33013 | -71.680130 | 43.299485 | 148582 | 140747.0 | 836.0 | 2563.0 | 354.0 | 0.0 | 209.0 | 810.0 | 5235.0 | 27866.0 | 32581.0 | 37811.0 | 9663 | 42.4 | 0.4185 | 69505 | 28.2 | 10.484782 |
| 453 | Rockingham County | NH | 05000US33015 | -71.099437 | 42.989360 | 303251 | 288104.0 | 2089.0 | 5846.0 | 57.0 | 0.0 | 1712.0 | 1695.0 | 7455.0 | 55536.0 | 64475.0 | 88648.0 | 10865 | 44.6 | 0.4134 | 81726 | 26.0 | 7.824510 |
| 454 | Strafford County | NH | 05000US33017 | -71.035927 | 43.293177 | 127428 | 118919.0 | 1196.0 | 4107.0 | 50.0 | 0.0 | 175.0 | 449.0 | 3463.0 | 22465.0 | 26634.0 | 29045.0 | 7332 | 36.9 | 0.4070 | 71295 | 25.0 | 11.339993 |
| 455 | Atlantic County | NJ | 05000US34001 | -74.633758 | 39.469354 | 270991 | 176909.0 | 41557.0 | 22599.0 | 824.0 | 60.0 | 20986.0 | 5476.0 | 13289.0 | 61796.0 | 51912.0 | 51545.0 | 38021 | 40.6 | 0.4599 | 56778 | 33.0 | 17.214834 |
| 456 | Bergen County | NJ | 05000US34003 | -74.074522 | 40.959090 | 939151 | 678573.0 | 56391.0 | 153276.0 | 1748.0 | 804.0 | 20616.0 | 20043.0 | 24347.0 | 147460.0 | 136290.0 | 325027.0 | 63589 | 41.4 | 0.4705 | 93683 | 29.9 | 8.823137 |
| 457 | Burlington County | NJ | 05000US34005 | -74.663006 | 39.875786 | 449284 | 323113.0 | 74530.0 | 23697.0 | 378.0 | 123.0 | 10123.0 | 3716.0 | 13659.0 | 93685.0 | 84528.0 | 117207.0 | 25271 | 41.5 | 0.4359 | 80254 | 29.8 | 9.893767 |
| 458 | Camden County | NJ | 05000US34007 | -74.961251 | 39.802352 | 510150 | 324529.0 | 96342.0 | 29696.0 | 1032.0 | 0.0 | 44228.0 | 9797.0 | 25365.0 | 106532.0 | 94158.0 | 109647.0 | 59392 | 38.5 | 0.4676 | 66362 | 31.4 | 13.428242 |
| 459 | Cape May County | NJ | 05000US34009 | -74.847716 | 39.086143 | 94430 | NaN | NaN | NaN | NaN | NaN | NaN | 1310.0 | 3930.0 | 22479.0 | 19412.0 | 22223.0 | 11002 | 49.0 | 0.4815 | 62548 | 38.8 | 12.477182 |
| 460 | Cumberland County | NJ | 05000US34011 | -75.121644 | 39.328387 | 153797 | 103640.0 | 28739.0 | 2258.0 | 1960.0 | 0.0 | 10350.0 | 6837.0 | 13298.0 | 40408.0 | 25704.0 | 14432.0 | 27031 | 36.8 | 0.4455 | 49110 | 38.0 | 21.164865 |
| 461 | Essex County | NJ | 05000US34013 | -74.246136 | 40.787216 | 796914 | 355482.0 | 317198.0 | 42298.0 | 1518.0 | 0.0 | 58747.0 | 27017.0 | 37024.0 | 148768.0 | 127211.0 | 182368.0 | 127170 | 37.2 | 0.5565 | 54277 | 33.4 | 17.856049 |
| 462 | Gloucester County | NJ | 05000US34015 | -75.143708 | 39.721019 | 292330 | 237650.0 | 29805.0 | 8955.0 | 215.0 | 0.0 | 6186.0 | 3824.0 | 9926.0 | 68480.0 | 54665.0 | 62232.0 | 21758 | 39.7 | 0.4097 | 79879 | 30.6 | 10.172047 |
| 463 | Hudson County | NJ | 05000US34017 | -74.078627 | 40.731384 | 677983 | 367911.0 | 81508.0 | 102554.0 | 2484.0 | 752.0 | 96113.0 | 34190.0 | 28839.0 | 127934.0 | 84016.0 | 196020.0 | 104668 | 34.9 | 0.5084 | 63808 | 28.4 | 13.378678 |
| 464 | Hunterdon County | NJ | 05000US34019 | -74.911969 | 40.565283 | 124676 | NaN | NaN | NaN | NaN | NaN | NaN | 1199.0 | 2724.0 | 19790.0 | 17615.0 | 46318.0 | 5694 | 46.2 | 0.4371 | 113684 | 31.8 | 7.689374 |
| 465 | Mercer County | NJ | 05000US34021 | -74.703724 | 40.282503 | 371023 | 238907.0 | 78453.0 | 40864.0 | 493.0 | 597.0 | 4213.0 | 11318.0 | 12975.0 | 63215.0 | 52544.0 | 106660.0 | 39443 | 38.5 | 0.5003 | 77650 | 32.0 | 15.523798 |
| 466 | Middlesex County | NJ | 05000US34023 | -74.407585 | 40.439592 | 837073 | 490322.0 | 83900.0 | 203045.0 | 2096.0 | 297.0 | 35789.0 | 19645.0 | 30188.0 | 152177.0 | 122684.0 | 242378.0 | 69774 | 38.2 | 0.4305 | 82375 | 28.8 | 9.272336 |
| 467 | Monmouth County | NJ | 05000US34025 | -74.152446 | 40.287056 | 625846 | 514689.0 | 45430.0 | 35774.0 | 966.0 | 30.0 | 18484.0 | 9590.0 | 19044.0 | 105325.0 | 104792.0 | 192457.0 | 42572 | 43.0 | 0.4670 | 90226 | 32.6 | 9.183209 |
| 468 | Morris County | NJ | 05000US34027 | -74.547427 | 40.858581 | 498423 | 402032.0 | 15796.0 | 53056.0 | 139.0 | 0.0 | 17716.0 | 8285.0 | 11168.0 | 70570.0 | 70205.0 | 184647.0 | 26458 | 42.4 | 0.4542 | 106985 | 27.4 | 6.818772 |
| 469 | Ocean County | NJ | 05000US34029 | -74.263027 | 39.865850 | 592497 | 536665.0 | 17336.0 | 11908.0 | 400.0 | 664.0 | 17161.0 | 7749.0 | 25063.0 | 136881.0 | 116812.0 | 117001.0 | 65390 | 42.8 | 0.4514 | 62222 | 37.0 | 16.585231 |
| 470 | Passaic County | NJ | 05000US34031 | -74.300308 | 41.033763 | 507945 | 333685.0 | 57390.0 | 26481.0 | 1643.0 | 180.0 | 74754.0 | 19956.0 | 28157.0 | 110652.0 | 69555.0 | 93237.0 | 87856 | 36.7 | 0.4780 | 62016 | 33.9 | 18.742524 |
| 471 | Salem County | NJ | 05000US34033 | -75.357356 | 39.573828 | 63436 | 50303.0 | 8147.0 | 774.0 | 162.0 | 0.0 | 1966.0 | 919.0 | 4395.0 | 17344.0 | 12545.0 | 8217.0 | 8287 | 41.2 | 0.4608 | 53662 | 33.1 | 22.430254 |
| 472 | Somerset County | NJ | 05000US34035 | -74.619930 | 40.565522 | 333751 | 224326.0 | 32448.0 | 57437.0 | 1718.0 | 47.0 | 10493.0 | 6072.0 | 8487.0 | 48748.0 | 46221.0 | 118651.0 | 16395 | 41.5 | 0.4574 | 104478 | 28.5 | 7.030275 |
| 473 | Sussex County | NJ | 05000US34037 | -74.691855 | 41.137424 | 142522 | 132461.0 | 2904.0 | 3229.0 | 0.0 | 0.0 | 1717.0 | 1016.0 | 3797.0 | 32778.0 | 28771.0 | 34509.0 | 8508 | 44.7 | 0.4099 | 87829 | 31.3 | 8.353534 |
| 474 | Union County | NJ | 05000US34039 | -74.308696 | 40.659871 | 555630 | 304604.0 | 114911.0 | 27892.0 | 2210.0 | 0.0 | 89983.0 | 21374.0 | 23500.0 | 107616.0 | 84409.0 | 131326.0 | 58356 | 38.5 | 0.5060 | 72028 | 31.6 | 13.358734 |
| 475 | Warren County | NJ | 05000US34041 | -75.009542 | 40.853524 | 106617 | 95037.0 | 4981.0 | 2825.0 | 117.0 | 0.0 | 2019.0 | 2627.0 | 4319.0 | 23435.0 | 20572.0 | 23700.0 | 9278 | 44.1 | 0.4152 | 74867 | 29.2 | 10.495656 |
| 476 | Bernalillo County | NM | 05000US35001 | -106.669065 | 35.054002 | 676953 | 479588.0 | 18679.0 | 18595.0 | 34521.0 | 498.0 | 101451.0 | 15166.0 | 31460.0 | 109164.0 | 147108.0 | 155675.0 | 110517 | 37.4 | 0.4730 | 50601 | 30.9 | 16.235400 |
| 477 | Chaves County | NM | 05000US35005 | -104.469837 | 33.361604 | 65282 | NaN | NaN | NaN | NaN | NaN | NaN | 4307.0 | 4607.0 | 10447.0 | 11332.0 | 9123.0 | 14534 | 35.1 | 0.4811 | 40960 | 26.5 | 28.308758 |
| 478 | Doña Ana County | NM | 05000US35013 | -106.832182 | 32.350912 | 214207 | 194538.0 | 3599.0 | 2341.0 | 2578.0 | 69.0 | 8652.0 | 11757.0 | 10972.0 | 30960.0 | 39175.0 | 34011.0 | 56426 | 33.5 | 0.4912 | 37496 | 31.1 | 24.609617 |
| 479 | Lea County | NM | 05000US35025 | -103.413271 | 32.795687 | 69749 | 62201.0 | 2172.0 | 0.0 | 452.0 | 46.0 | 3134.0 | 5819.0 | 5474.0 | 12300.0 | 10986.0 | 5961.0 | 14352 | 31.5 | 0.4445 | 55636 | 22.9 | 21.584187 |
| 480 | McKinley County | NM | 05000US35031 | -108.255307 | 35.573732 | 74923 | NaN | NaN | NaN | NaN | NaN | NaN | 2471.0 | 7363.0 | 15187.0 | 13109.0 | 4374.0 | 27363 | 31.6 | 0.4899 | 31526 | 24.7 | 46.812223 |
| 481 | Otero County | NM | 05000US35035 | -105.781078 | 32.588776 | 65410 | 50841.0 | 2625.0 | 969.0 | 4423.0 | 0.0 | 3628.0 | 1472.0 | 3841.0 | 12436.0 | 16968.0 | 7790.0 | 13261 | 35.8 | 0.5330 | 43646 | 23.7 | 17.324875 |
| 482 | Sandoval County | NM | 05000US35043 | -106.882618 | 35.685073 | 142025 | 99399.0 | 2980.0 | 2100.0 | 18026.0 | 25.0 | 11878.0 | 2191.0 | 7989.0 | 21426.0 | 34181.0 | 30855.0 | 22909 | 38.8 | 0.4236 | 57158 | 32.0 | 16.007306 |
| 483 | San Juan County | NM | 05000US35045 | -108.324578 | 36.511624 | 115079 | 62316.0 | 656.0 | 690.0 | 41426.0 | 74.0 | 6795.0 | 2403.0 | 6546.0 | 21690.0 | 30389.0 | 12547.0 | 19154 | 36.3 | 0.4229 | 52003 | 23.0 | 33.464832 |
| 484 | Santa Fe County | NM | 05000US35049 | -105.966441 | 35.513722 | 148651 | 126078.0 | 1260.0 | 1976.0 | 5173.0 | 38.0 | 9950.0 | 3914.0 | 5524.0 | 26507.0 | 26070.0 | 45718.0 | 20939 | 46.0 | 0.4794 | 57863 | 26.7 | 18.698416 |
| 485 | Valencia County | NM | 05000US35061 | -106.806582 | 34.716840 | 75626 | 60577.0 | 788.0 | 340.0 | 2782.0 | 0.0 | 8365.0 | 1608.0 | 6200.0 | 17246.0 | 16503.0 | 9159.0 | 13418 | 38.3 | 0.4164 | 43700 | 31.5 | 28.398871 |
| 486 | Clark County | NV | 05000US32003 | -115.013819 | 36.214236 | 2155664 | 1330054.0 | 247427.0 | 211925.0 | 13583.0 | 17783.0 | 223704.0 | 79424.0 | 117030.0 | 434379.0 | 476888.0 | 340777.0 | 302789 | 37.0 | 0.4563 | 54384 | 30.6 | 16.305686 |
| 487 | Washoe County | NV | 05000US32031 | -119.710315 | 40.703311 | 453616 | 355315.0 | 10627.0 | 25562.0 | 6826.0 | 2436.0 | 32942.0 | 13418.0 | 21828.0 | 76677.0 | 105659.0 | 91097.0 | 54745 | 38.2 | 0.4741 | 58175 | 29.4 | 13.798175 |
| 488 | Albany County | NY | 05000US36001 | -73.974014 | 42.588271 | 308846 | 230831.0 | 38386.0 | 21011.0 | 788.0 | 0.0 | 5538.0 | 3916.0 | 12078.0 | 46857.0 | 53402.0 | 87343.0 | 35650 | 37.9 | 0.4635 | 61754 | 29.6 | 12.602031 |
| 489 | Bronx County | NY | 05000US36005 | -73.852939 | 40.848711 | 1455720 | 312877.0 | 495031.0 | 51958.0 | 8777.0 | 372.0 | 528863.0 | 95244.0 | 130651.0 | 266034.0 | 233297.0 | 170921.0 | 407377 | 33.6 | 0.4926 | 37525 | 34.9 | 21.305053 |
| 490 | Broome County | NY | 05000US36007 | -75.830291 | 42.161977 | 195334 | 166572.0 | 12650.0 | 7006.0 | 13.0 | 21.0 | 1866.0 | 2417.0 | 9308.0 | 40350.0 | 38591.0 | 38620.0 | 30474 | 39.6 | 0.4657 | 50463 | 33.1 | 14.862845 |
| 491 | Cattaraugus County | NY | 05000US36009 | -78.681006 | 42.244853 | 77677 | 71286.0 | 1148.0 | 509.0 | 2615.0 | 0.0 | 376.0 | 1040.0 | 4332.0 | 20809.0 | 15639.0 | 9896.0 | 10508 | 41.6 | 0.4167 | 46842 | 27.0 | 22.452163 |
| 492 | Cayuga County | NY | 05000US36011 | -76.574587 | 43.008546 | 77861 | NaN | NaN | NaN | NaN | NaN | NaN | 861.0 | 5524.0 | 17112.0 | 17667.0 | 12731.0 | 8372 | 41.4 | 0.4138 | 54995 | 27.0 | 18.077892 |
| 493 | Chautauqua County | NY | 05000US36013 | -79.407595 | 42.304216 | 129504 | 120008.0 | 3784.0 | 760.0 | 326.0 | 64.0 | 2035.0 | 1653.0 | 7110.0 | 32249.0 | 27233.0 | 19214.0 | 24899 | 42.2 | 0.4303 | 42204 | 30.8 | 20.065758 |
| 494 | Chemung County | NY | 05000US36015 | -76.747179 | 42.155281 | 86322 | 75851.0 | 4798.0 | 1588.0 | 171.0 | 22.0 | 770.0 | 1162.0 | 3196.0 | 20103.0 | 18312.0 | 16578.0 | 11262 | 41.5 | 0.4492 | 51269 | 30.9 | 15.718519 |
| 495 | Clinton County | NY | 05000US36019 | -73.705648 | 44.752710 | 81073 | NaN | NaN | NaN | NaN | NaN | NaN | 1211.0 | 4416.0 | 21300.0 | 16463.0 | 11902.0 | 9985 | 39.2 | 0.4077 | 55316 | 28.4 | 16.297675 |
| 496 | Dutchess County | NY | 05000US36027 | -73.739951 | 41.755009 | 294473 | 232515.0 | 31588.0 | 10743.0 | 989.0 | 422.0 | 9189.0 | 3652.0 | 15775.0 | 53669.0 | 58342.0 | 70870.0 | 24873 | 41.8 | 0.4587 | 74115 | 33.1 | 9.334566 |
| 497 | Erie County | NY | 05000US36029 | -78.778192 | 42.752759 | 921046 | 718970.0 | 122691.0 | 34132.0 | 4730.0 | 405.0 | 18740.0 | 10762.0 | 38779.0 | 181997.0 | 192839.0 | 213785.0 | 127064 | 40.0 | 0.4723 | 54246 | 29.5 | 15.971436 |
| 498 | Jefferson County | NY | 05000US36045 | -76.053522 | 43.995371 | 114006 | 98583.0 | 5871.0 | 1998.0 | 1667.0 | 41.0 | 1425.0 | 1070.0 | 3885.0 | 27174.0 | 24037.0 | 14883.0 | 17083 | 33.1 | 0.4343 | 45624 | 30.4 | 14.811059 |
| 499 | Kings County | NY | 05000US36047 | -73.950777 | 40.635133 | 2629150 | 1124155.0 | 861749.0 | 315850.0 | 8714.0 | 1402.0 | 237591.0 | 115173.0 | 176092.0 | 450548.0 | 353301.0 | 651985.0 | 536846 | 34.8 | 0.5252 | 55150 | 32.3 | 17.715483 |
| 500 | Livingston County | NY | 05000US36051 | -77.769779 | 42.727484 | 64257 | 59522.0 | 1985.0 | 699.0 | 352.0 | 76.0 | 800.0 | 326.0 | 3790.0 | 14264.0 | 12995.0 | 10841.0 | 8542 | 40.7 | 0.3850 | 53121 | 29.0 | 12.918340 |
| 501 | Madison County | NY | 05000US36053 | -75.663575 | 42.910026 | 71329 | 67495.0 | 1272.0 | 726.0 | 203.0 | 152.0 | 200.0 | 688.0 | 3141.0 | 16434.0 | 13395.0 | 13987.0 | 5456 | 41.3 | 0.4330 | 60630 | 28.4 | 17.843199 |
| 502 | Monroe County | NY | 05000US36055 | -77.664656 | 43.464475 | 747727 | 570709.0 | 113806.0 | 26420.0 | 2763.0 | 309.0 | 12100.0 | 10822.0 | 34667.0 | 116117.0 | 153586.0 | 191689.0 | 108069 | 38.7 | 0.4704 | 54492 | 31.9 | 13.607866 |
| 503 | Nassau County | NY | 05000US36059 | -73.589384 | 40.729687 | 1361500 | 926594.0 | 158561.0 | 127934.0 | 3420.0 | 285.0 | 102363.0 | 35671.0 | 33808.0 | 217826.0 | 220785.0 | 423371.0 | 79322 | 41.5 | 0.4578 | 105870 | 32.7 | 8.294520 |
| 504 | New York County | NY | 05000US36061 | -73.970174 | 40.776557 | 1643734 | 931953.0 | 246095.0 | 204434.0 | 4555.0 | 1205.0 | 179384.0 | 66463.0 | 70662.0 | 158391.0 | 176052.0 | 755940.0 | 275840 | 37.1 | 0.5945 | 77559 | 28.1 | 13.225301 |
| 505 | Niagara County | NY | 05000US36063 | -78.792143 | 43.456731 | 211758 | 185171.0 | 13450.0 | 2082.0 | 2246.0 | 141.0 | 1822.0 | 2109.0 | 9344.0 | 51170.0 | 50068.0 | 36715.0 | 25684 | 43.4 | 0.4245 | 50233 | 27.6 | 18.992695 |
| 506 | Oneida County | NY | 05000US36065 | -75.434282 | 43.242727 | 231190 | 197816.0 | 13313.0 | 9824.0 | 400.0 | 53.0 | 2474.0 | 4129.0 | 12995.0 | 51483.0 | 45886.0 | 42468.0 | 35168 | 41.1 | 0.4519 | 52996 | 26.9 | 16.003821 |
| 507 | Onondaga County | NY | 05000US36067 | -76.196117 | 43.006530 | 466194 | 370920.0 | 52781.0 | 17413.0 | 1769.0 | 81.0 | 6250.0 | 6571.0 | 19689.0 | 79703.0 | 96216.0 | 110393.0 | 66380 | 38.7 | 0.4653 | 56991 | 28.5 | 16.157697 |
| 508 | Ontario County | NY | 05000US36069 | -77.303277 | 42.856695 | 109828 | 101231.0 | 3180.0 | 1345.0 | 584.0 | 114.0 | 1649.0 | 1060.0 | 3715.0 | 21960.0 | 24107.0 | 26228.0 | 9893 | 44.3 | 0.4334 | 57448 | 30.8 | 11.534291 |
| 509 | Orange County | NY | 05000US36071 | -74.306252 | 41.402410 | 379210 | 278706.0 | 38853.0 | 10686.0 | 1877.0 | 149.0 | 32984.0 | 7502.0 | 15919.0 | 71627.0 | 74219.0 | 70091.0 | 46300 | 37.2 | 0.4395 | 73025 | 32.2 | 16.832710 |
| 510 | Oswego County | NY | 05000US36075 | -76.209258 | 43.461443 | 118987 | 113860.0 | 1795.0 | 730.0 | 184.0 | 75.0 | 167.0 | 972.0 | 7435.0 | 30988.0 | 24513.0 | 14223.0 | 20266 | 40.1 | 0.4212 | 53562 | 32.1 | 13.136020 |
| 511 | Putnam County | NY | 05000US36079 | -73.743882 | 41.427903 | 98900 | 85102.0 | 1490.0 | 2214.0 | 859.0 | 56.0 | 7046.0 | 2746.0 | 2290.0 | 17818.0 | 17031.0 | 30585.0 | 5685 | 43.8 | 0.4237 | 96992 | 31.7 | 4.594097 |
| 512 | Queens County | NY | 05000US36081 | -73.837929 | 40.658557 | 2333054 | 905860.0 | 421307.0 | 592922.0 | 9509.0 | 1240.0 | 318657.0 | 135253.0 | 138116.0 | 463579.0 | 369631.0 | 522093.0 | 303895 | 38.3 | 0.4564 | 62207 | 33.2 | 15.859148 |
| 513 | Rensselaer County | NY | 05000US36083 | -73.513845 | 42.710421 | 160070 | 137956.0 | 9755.0 | 4019.0 | 799.0 | 0.0 | 1866.0 | 2522.0 | 5690.0 | 29778.0 | 34629.0 | 36634.0 | 18023 | 39.5 | 0.4264 | 65965 | 27.6 | 13.034896 |
| 514 | Richmond County | NY | 05000US36085 | -74.137063 | 40.563855 | 476015 | 352530.0 | 48920.0 | 40747.0 | 251.0 | 721.0 | 22464.0 | 12602.0 | 22067.0 | 100582.0 | 84304.0 | 107267.0 | 62239 | 39.7 | 0.4580 | 77197 | 33.8 | 16.483149 |
| 515 | Rockland County | NY | 05000US36087 | -74.024772 | 41.154785 | 326780 | 225848.0 | 40350.0 | 19686.0 | 592.0 | 49.0 | 33989.0 | 12081.0 | 14296.0 | 44460.0 | 50101.0 | 81219.0 | 47860 | 36.5 | 0.4643 | 85515 | 37.6 | 18.084367 |
| 516 | St. Lawrence County | NY | 05000US36089 | -75.074311 | 44.488112 | 110038 | 102840.0 | 2925.0 | 1489.0 | 437.0 | 11.0 | 472.0 | 883.0 | 6443.0 | 25470.0 | 20783.0 | 16887.0 | 16475 | 38.8 | 0.4378 | 51592 | 27.1 | 16.509795 |
| 517 | Saratoga County | NY | 05000US36091 | -73.855387 | 43.106135 | 227053 | 211505.0 | 2719.0 | 6797.0 | 484.0 | 91.0 | 907.0 | 1857.0 | 6924.0 | 39007.0 | 45963.0 | 65271.0 | 12776 | 42.0 | 0.4455 | 76097 | 25.1 | 9.254773 |
| 518 | Schenectady County | NY | 05000US36093 | -74.043583 | 42.817542 | 154553 | 119476.0 | 14509.0 | 6739.0 | 258.0 | 219.0 | 7110.0 | 2429.0 | 7426.0 | 34772.0 | 27632.0 | 32144.0 | 14771 | 40.1 | 0.4518 | 58331 | 31.2 | 11.586617 |
| 519 | Steuben County | NY | 05000US36101 | -77.385525 | 42.266725 | 96940 | 91500.0 | 1835.0 | 1298.0 | 120.0 | 0.0 | 524.0 | 721.0 | 5018.0 | 24601.0 | 20770.0 | 15467.0 | 11689 | 42.3 | 0.4569 | 50575 | 25.5 | 16.516720 |
| 520 | Suffolk County | NY | 05000US36103 | -72.692218 | 40.943554 | 1492583 | 1195796.0 | 111177.0 | 58726.0 | 3361.0 | 833.0 | 93130.0 | 31760.0 | 53057.0 | 298638.0 | 278375.0 | 359095.0 | 106175 | 41.2 | 0.4433 | 92933 | 35.4 | 9.643673 |
| 521 | Sullivan County | NY | 05000US36105 | -74.764680 | 41.720176 | 74801 | NaN | NaN | NaN | NaN | NaN | NaN | 1736.0 | 4974.0 | 15843.0 | 16922.0 | 11500.0 | 10852 | 43.8 | 0.4915 | 50652 | 30.6 | 16.759219 |
| 522 | Tompkins County | NY | 05000US36109 | -76.473712 | 42.453281 | 104871 | 83423.0 | 4698.0 | 10625.0 | 268.0 | 0.0 | 1656.0 | 445.0 | 2604.0 | 13082.0 | 12885.0 | 31343.0 | 18347 | 30.6 | 0.4750 | 56349 | 38.8 | 7.555131 |
| 523 | Ulster County | NY | 05000US36111 | -74.265447 | 41.947232 | 179225 | 152042.0 | 10859.0 | 3969.0 | 151.0 | 21.0 | 5275.0 | 3048.0 | 7130.0 | 38601.0 | 36063.0 | 42436.0 | 24059 | 44.1 | 0.4610 | 62790 | 34.3 | 11.186272 |
| 524 | Warren County | NY | 05000US36113 | -73.838139 | 43.555105 | 64567 | NaN | NaN | NaN | NaN | NaN | NaN | 510.0 | 2436.0 | 16120.0 | 13823.0 | 14334.0 | 7473 | 47.1 | 0.4397 | 58061 | 30.3 | 15.564648 |
| 525 | Wayne County | NY | 05000US36117 | -77.063164 | 43.458758 | 90798 | NaN | NaN | NaN | NaN | NaN | NaN | 1355.0 | 4074.0 | 20999.0 | 22531.0 | 13885.0 | 8312 | 43.6 | 0.3927 | 59538 | 26.1 | 13.820141 |
| 526 | Westchester County | NY | 05000US36119 | -73.745912 | 41.152770 | 974542 | 628687.0 | 144510.0 | 57962.0 | 2745.0 | 192.0 | 107885.0 | 34299.0 | 39242.0 | 129113.0 | 135204.0 | 317447.0 | 94613 | 40.7 | 0.5395 | 89709 | 32.3 | 10.937244 |
| 527 | Allen County | OH | 05000US39003 | -84.106546 | 40.771528 | 103742 | NaN | NaN | NaN | NaN | NaN | NaN | 643.0 | 7003.0 | 25853.0 | 23140.0 | 11378.0 | 14916 | 38.2 | 0.4683 | 47592 | 29.1 | 17.605422 |
| 528 | Ashtabula County | OH | 05000US39007 | -80.745641 | 41.906644 | 98231 | NaN | NaN | NaN | NaN | NaN | NaN | 1158.0 | 6422.0 | 29387.0 | 19749.0 | 9353.0 | 17613 | 43.4 | 0.4635 | 42965 | 31.6 | 22.105453 |
| 529 | Athens County | OH | 05000US39009 | -82.046008 | 39.333848 | 66186 | NaN | NaN | NaN | NaN | NaN | NaN | 640.0 | 3574.0 | 13088.0 | 8906.0 | 9331.0 | 16283 | 27.9 | 0.4999 | 36193 | 30.0 | 17.076201 |
| 530 | Belmont County | OH | 05000US39013 | -80.967727 | 40.017682 | 68673 | NaN | NaN | NaN | NaN | NaN | NaN | 588.0 | 3639.0 | 19981.0 | 15876.0 | 9679.0 | 10051 | 44.5 | 0.4583 | 48220 | 26.6 | 24.045521 |
| 531 | Butler County | OH | 05000US39017 | -84.565397 | 39.439915 | 377537 | 322458.0 | 30413.0 | 10086.0 | 536.0 | 98.0 | 3219.0 | 3758.0 | 17979.0 | 77595.0 | 67478.0 | 71670.0 | 45591 | 36.5 | 0.4410 | 63273 | 27.5 | 11.132150 |
| 532 | Clark County | OH | 05000US39023 | -83.783676 | 39.917032 | 134786 | 117082.0 | 9921.0 | 393.0 | 289.0 | 53.0 | 808.0 | 654.0 | 9272.0 | 35795.0 | 29195.0 | 16491.0 | 20474 | 41.9 | 0.4374 | 46811 | 28.3 | 16.645890 |
| 533 | Clermont County | OH | 05000US39025 | -84.149614 | 39.052084 | 203022 | NaN | NaN | NaN | NaN | NaN | NaN | 2648.0 | 11791.0 | 43477.0 | 39486.0 | 39137.0 | 22135 | 39.7 | 0.4489 | 60661 | 27.8 | 13.763234 |
| 534 | Columbiana County | OH | 05000US39029 | -80.777231 | 40.768462 | 103685 | 97992.0 | 2766.0 | 0.0 | 201.0 | 0.0 | 1185.0 | 1168.0 | 6630.0 | 32453.0 | 23781.0 | 9783.0 | 18398 | 44.2 | 0.4132 | 47864 | 25.7 | 19.199884 |
| 535 | Cuyahoga County | OH | 05000US39035 | -81.724217 | 41.760392 | 1249352 | 780502.0 | 367135.0 | 35357.0 | 2275.0 | 389.0 | 21144.0 | 12355.0 | 65203.0 | 252767.0 | 259575.0 | 271844.0 | 221528 | 40.2 | 0.5163 | 46601 | 29.9 | 19.232533 |
| 536 | Delaware County | OH | 05000US39041 | -83.007462 | 40.278941 | 196463 | 173006.0 | 6944.0 | 11311.0 | 253.0 | 53.0 | 738.0 | 999.0 | 3494.0 | 25715.0 | 27274.0 | 70232.0 | 9934 | 38.1 | 0.4478 | 101693 | 24.9 | 5.242141 |
| 537 | Erie County | OH | 05000US39043 | -82.525897 | 41.554006 | 75107 | NaN | NaN | NaN | NaN | NaN | NaN | 391.0 | 3588.0 | 20144.0 | 17534.0 | 11619.0 | 8844 | 46.2 | 0.4725 | 48949 | 28.6 | 16.159312 |
| 538 | Fairfield County | OH | 05000US39045 | -82.628276 | 39.752935 | 152597 | 133473.0 | 11068.0 | 1560.0 | 407.0 | 0.0 | 1309.0 | 1094.0 | 5594.0 | 33519.0 | 33900.0 | 28214.0 | 14992 | 40.2 | 0.4207 | 65316 | 27.6 | 10.630417 |
| 539 | Franklin County | OH | 05000US39049 | -83.008258 | 39.969447 | 1264518 | 855234.0 | 280522.0 | 62789.0 | 2170.0 | 747.0 | 15363.0 | 16185.0 | 54211.0 | 214836.0 | 220243.0 | 329161.0 | 205259 | 34.0 | 0.4644 | 56055 | 27.6 | 11.262755 |
| 540 | Geauga County | OH | 05000US39055 | -81.173505 | 41.499322 | 94060 | NaN | NaN | NaN | NaN | NaN | NaN | 887.0 | 1550.0 | 16813.0 | 16813.0 | 23217.0 | 4260 | 44.9 | 0.4212 | 76384 | 28.0 | 17.841155 |
| 541 | Greene County | OH | 05000US39057 | -83.894894 | 39.687478 | 164765 | 141014.0 | 11672.0 | 4902.0 | 235.0 | 0.0 | 1707.0 | 1216.0 | 4664.0 | 27002.0 | 35309.0 | 40591.0 | 20151 | 38.6 | 0.4557 | 62018 | 26.4 | 9.926770 |
| 542 | Hamilton County | OH | 05000US39061 | -84.544187 | 39.196927 | 809099 | 542878.0 | 208972.0 | 21398.0 | 800.0 | 85.0 | 11962.0 | 9642.0 | 34660.0 | 145662.0 | 150053.0 | 201314.0 | 125214 | 37.0 | 0.5009 | 53229 | 27.8 | 15.253329 |
| 543 | Hancock County | OH | 05000US39063 | -83.666033 | 41.000471 | 75872 | NaN | NaN | NaN | NaN | NaN | NaN | 465.0 | 2109.0 | 18555.0 | 16879.0 | 13260.0 | 7457 | 38.2 | 0.4264 | 52810 | 25.4 | 12.415991 |
| 544 | Jefferson County | OH | 05000US39081 | -80.761410 | 40.399188 | 66704 | NaN | NaN | NaN | NaN | NaN | NaN | 578.0 | 4003.0 | 20785.0 | 13074.0 | 7764.0 | 9614 | 44.6 | 0.4539 | 44257 | 26.0 | 23.925698 |
| 545 | Lake County | OH | 05000US39085 | -81.392643 | 41.924116 | 228614 | 208258.0 | 9011.0 | 3153.0 | 388.0 | 26.0 | 2414.0 | 2083.0 | 9488.0 | 52724.0 | 52391.0 | 47296.0 | 18962 | 43.7 | 0.4228 | 61870 | 25.0 | 11.570205 |
| 546 | Licking County | OH | 05000US39089 | -82.481251 | 40.093609 | 172198 | NaN | NaN | NaN | NaN | NaN | NaN | 719.0 | 8017.0 | 39583.0 | 37301.0 | 28906.0 | 21237 | 40.0 | 0.4562 | 58685 | 27.7 | 13.776273 |
| 547 | Lorain County | OH | 05000US39093 | -82.179722 | 41.438804 | 306365 | 260616.0 | 26803.0 | 2735.0 | 578.0 | 174.0 | 4160.0 | 2994.0 | 18362.0 | 63625.0 | 71303.0 | 51544.0 | 36299 | 41.3 | 0.4665 | 54504 | 29.7 | 13.838858 |
| 548 | Lucas County | OH | 05000US39095 | -83.468867 | 41.682321 | 432488 | 313144.0 | 82833.0 | 6710.0 | 816.0 | 233.0 | 10626.0 | 3614.0 | 23969.0 | 82241.0 | 102925.0 | 76367.0 | 83846 | 38.0 | 0.4933 | 44534 | 29.5 | 14.848766 |
| 549 | Mahoning County | OH | 05000US39099 | -80.770396 | 41.010880 | 230008 | 183773.0 | 35050.0 | 2048.0 | 402.0 | 0.0 | 1527.0 | 2102.0 | 11681.0 | 61280.0 | 46477.0 | 41178.0 | 41781 | 43.5 | 0.4745 | 42295 | 28.3 | 17.613765 |
| 550 | Marion County | OH | 05000US39101 | -83.172927 | 40.588208 | 65096 | NaN | NaN | NaN | NaN | NaN | NaN | 226.0 | 5225.0 | 21017.0 | 13564.0 | 5378.0 | 7109 | 41.8 | 0.4307 | 42826 | 31.2 | 23.620291 |
| 551 | Medina County | OH | 05000US39103 | -81.899566 | 41.116051 | 177221 | 168281.0 | 2542.0 | 1651.0 | 524.0 | 0.0 | 996.0 | 745.0 | 4842.0 | 38245.0 | 38053.0 | 40838.0 | 11080 | 42.3 | 0.4221 | 72618 | 24.1 | 9.569103 |
| 552 | Miami County | OH | 05000US39109 | -84.228414 | 40.053326 | 104679 | NaN | NaN | NaN | NaN | NaN | NaN | 1340.0 | 5397.0 | 26659.0 | 22779.0 | 15818.0 | 9827 | 41.7 | 0.3901 | 60170 | 25.1 | 12.995909 |
| 553 | Montgomery County | OH | 05000US39113 | -84.290545 | 39.755218 | 531239 | 388711.0 | 110639.0 | 11335.0 | 640.0 | 374.0 | 3071.0 | 5431.0 | 27512.0 | 101173.0 | 132038.0 | 93625.0 | 94861 | 39.3 | 0.4824 | 46936 | 30.2 | 14.987160 |
| 554 | Muskingum County | OH | 05000US39119 | -81.943506 | 39.966046 | 86068 | NaN | NaN | NaN | NaN | NaN | NaN | 1357.0 | 6274.0 | 24485.0 | 18375.0 | 7427.0 | 10853 | 40.1 | 0.4240 | 43422 | 29.9 | 23.849103 |
| 555 | Portage County | OH | 05000US39133 | -81.196932 | 41.168640 | 161921 | 147855.0 | 6943.0 | 2837.0 | 174.0 | 0.0 | 511.0 | 1019.0 | 5375.0 | 37568.0 | 28361.0 | 30776.0 | 22213 | 37.4 | 0.4712 | 49695 | 33.9 | 16.187488 |
| 556 | Richland County | OH | 05000US39139 | -82.542715 | 40.774167 | 121107 | 104750.0 | 12153.0 | 821.0 | 253.0 | 85.0 | 630.0 | 710.0 | 8784.0 | 33975.0 | 25054.0 | 14961.0 | 17699 | 41.3 | 0.4246 | 44073 | 26.6 | 23.482049 |
| 557 | Ross County | OH | 05000US39141 | -83.059585 | 39.323763 | 77000 | NaN | NaN | NaN | NaN | NaN | NaN | 710.0 | 5176.0 | 23946.0 | 15183.0 | 8774.0 | 14125 | 40.9 | 0.4579 | 46422 | 29.0 | 20.588645 |
| 558 | Scioto County | OH | 05000US39145 | -82.999028 | 38.815019 | 76088 | NaN | NaN | NaN | NaN | NaN | NaN | 834.0 | 4837.0 | 22310.0 | 16365.0 | 7300.0 | 15073 | 39.7 | 0.4785 | 39210 | 31.3 | 25.270867 |
| 559 | Stark County | OH | 05000US39151 | -81.365667 | 40.814131 | 373612 | 327583.0 | 27377.0 | 3682.0 | 218.0 | 0.0 | 2474.0 | 3676.0 | 16471.0 | 99899.0 | 74191.0 | 62887.0 | 47594 | 41.6 | 0.4306 | 50994 | 26.4 | 15.003057 |
| 560 | Summit County | OH | 05000US39153 | -81.534936 | 41.121851 | 540300 | 427390.0 | 77916.0 | 17116.0 | 503.0 | 149.0 | 1385.0 | 6802.0 | 22042.0 | 119225.0 | 108420.0 | 118848.0 | 71951 | 41.1 | 0.4657 | 52036 | 28.4 | 17.357180 |
| 561 | Trumbull County | OH | 05000US39155 | -80.767656 | 41.308936 | 201825 | 178222.0 | 17537.0 | 860.0 | 364.0 | 0.0 | 291.0 | 1687.0 | 9650.0 | 63967.0 | 36585.0 | 28141.0 | 36379 | 44.2 | 0.4613 | 45552 | 29.5 | 20.630777 |
| 562 | Tuscarawas County | OH | 05000US39157 | -81.471157 | 40.447441 | 92420 | NaN | NaN | NaN | NaN | NaN | NaN | 1475.0 | 4591.0 | 27681.0 | 15126.0 | 11863.0 | 12388 | 39.3 | 0.3900 | 50440 | 24.5 | 21.130748 |
| 563 | Warren County | OH | 05000US39165 | -84.169906 | 39.425652 | 227063 | NaN | NaN | NaN | NaN | NaN | NaN | 1264.0 | 8084.0 | 39305.0 | 36571.0 | 65314.0 | 10777 | 39.0 | 0.4504 | 80207 | 26.1 | 8.061502 |
| 564 | Wayne County | OH | 05000US39169 | -81.887194 | 40.829661 | 116470 | NaN | NaN | NaN | NaN | NaN | NaN | 852.0 | 5149.0 | 30689.0 | 20550.0 | 15730.0 | 13883 | 38.7 | 0.4116 | 53434 | 26.7 | 16.447536 |
| 565 | Wood County | OH | 05000US39173 | -83.622682 | 41.360183 | 130219 | 119109.0 | 3912.0 | 1938.0 | 510.0 | 234.0 | 1858.0 | 348.0 | 4275.0 | 22856.0 | 25074.0 | 27864.0 | 15181 | 34.8 | 0.4461 | 60166 | 26.7 | 12.109616 |
| 566 | Canadian County | OK | 05000US40017 | -97.979836 | 35.543416 | 136532 | 110859.0 | 3031.0 | 4298.0 | 5720.0 | 0.0 | 5825.0 | 1868.0 | 5094.0 | 26272.0 | 32066.0 | 23568.0 | 11644 | 35.8 | 0.3921 | 67177 | 27.6 | 15.064994 |
| 567 | Cleveland County | OK | 05000US40027 | -97.328332 | 35.203117 | 278655 | 218891.0 | 12353.0 | 12864.0 | 11785.0 | 142.0 | 3154.0 | 1558.0 | 10788.0 | 40921.0 | 62470.0 | 58412.0 | 34173 | 34.0 | 0.4259 | 61288 | 27.8 | 10.038819 |
| 568 | Comanche County | OK | 05000US40031 | -98.476597 | 34.662628 | 122136 | 76393.0 | 22659.0 | 2645.0 | 6613.0 | 225.0 | 2850.0 | 1104.0 | 7015.0 | 24924.0 | 26625.0 | 15609.0 | 16655 | 33.3 | 0.4348 | 51164 | 25.4 | 12.487787 |
| 569 | Creek County | OK | 05000US40037 | -96.379793 | 35.907732 | 71312 | 56935.0 | 1349.0 | 249.0 | 8304.0 | 55.0 | 282.0 | 599.0 | 5005.0 | 20216.0 | 13638.0 | 7807.0 | 10936 | 40.0 | 0.4342 | 43609 | 27.4 | 20.675813 |
| 570 | Muskogee County | OK | 05000US40101 | -95.383911 | 35.617551 | 69477 | 40817.0 | 7297.0 | 513.0 | 12438.0 | 103.0 | 1728.0 | 540.0 | 4641.0 | 16266.0 | 14042.0 | 9460.0 | 14280 | 37.9 | 0.4802 | 40860 | 31.1 | 21.815385 |
| 571 | Oklahoma County | OK | 05000US40109 | -97.409401 | 35.554611 | 782970 | 539756.0 | 118634.0 | 24246.0 | 24989.0 | 1011.0 | 24272.0 | 20703.0 | 39331.0 | 129350.0 | 154044.0 | 159102.0 | 125913 | 34.5 | 0.4810 | 51078 | 28.1 | 15.139512 |
| 572 | Payne County | OK | 05000US40119 | -96.975255 | 36.079225 | 81131 | NaN | NaN | NaN | NaN | NaN | NaN | 649.0 | 2418.0 | 11188.0 | 13122.0 | 16090.0 | 20462 | 27.3 | 0.5589 | 35927 | 41.3 | 13.830447 |
| 573 | Pottawatomie County | OK | 05000US40125 | -96.957007 | 35.211393 | 72290 | 54566.0 | 2607.0 | 578.0 | 10306.0 | 60.0 | 368.0 | 355.0 | 4952.0 | 17509.0 | 15994.0 | 8057.0 | 12639 | 37.7 | 0.4357 | 41716 | 27.5 | 21.505461 |
| 574 | Rogers County | OK | 05000US40131 | -95.601337 | 36.378082 | 91766 | 68761.0 | 871.0 | 1044.0 | 10913.0 | 0.0 | 1171.0 | 701.0 | 3580.0 | 20763.0 | 21981.0 | 14149.0 | 7558 | 39.4 | 0.4135 | 62434 | 28.4 | 16.923528 |
| 575 | Tulsa County | OK | 05000US40143 | -95.941731 | 36.120120 | 642940 | 445786.0 | 63930.0 | 21241.0 | 32602.0 | 279.0 | 26455.0 | 12916.0 | 26963.0 | 109245.0 | 135265.0 | 131420.0 | 101057 | 35.5 | 0.4845 | 51325 | 27.9 | 14.113482 |
| 576 | Wagoner County | OK | 05000US40145 | -95.514100 | 35.963479 | 77679 | 58594.0 | 2716.0 | 951.0 | 6890.0 | 90.0 | 908.0 | 490.0 | 3919.0 | 17586.0 | 16697.0 | 13188.0 | 9540 | 39.1 | 0.3806 | 62041 | 25.2 | 10.587154 |
| 577 | Benton County | OR | 05000US41003 | -123.426317 | 44.490623 | 89385 | 77080.0 | 984.0 | 5884.0 | 459.0 | 262.0 | 1185.0 | 377.0 | 1634.0 | 5657.0 | 16729.0 | 29171.0 | 17134 | 32.5 | 0.4911 | 55459 | 41.7 | 6.811800 |
| 578 | Clackamas County | OR | 05000US41005 | -122.195127 | 45.160493 | 408062 | 357854.0 | 4548.0 | 16159.0 | 3639.0 | 626.0 | 8713.0 | 4545.0 | 11297.0 | 58999.0 | 106984.0 | 103803.0 | 35647 | 41.3 | 0.4488 | 74891 | 29.6 | 7.769196 |
| 579 | Deschutes County | OR | 05000US41017 | -121.225575 | 43.915118 | 181307 | 167962.0 | 1089.0 | 1730.0 | 806.0 | 87.0 | 3582.0 | 816.0 | 8540.0 | 36923.0 | 41362.0 | 43093.0 | 19270 | 41.8 | 0.4200 | 61870 | 30.3 | 11.154613 |
| 580 | Douglas County | OR | 05000US41019 | -123.154380 | 43.285903 | 108457 | 100124.0 | 642.0 | 1026.0 | 1033.0 | 102.0 | 618.0 | 852.0 | 6552.0 | 24933.0 | 31953.0 | 13908.0 | 15885 | 47.2 | 0.4685 | 42889 | 29.4 | 15.436579 |
| 581 | Jackson County | OR | 05000US41029 | -122.675797 | 42.411782 | 216527 | 192932.0 | 1390.0 | 2803.0 | 2298.0 | 635.0 | 8039.0 | 4260.0 | 11143.0 | 38812.0 | 57243.0 | 40976.0 | 30792 | 43.1 | 0.4666 | 48563 | 34.5 | 11.274425 |
| 582 | Josephine County | OR | 05000US41033 | -123.597245 | 42.385382 | 85904 | NaN | NaN | NaN | NaN | NaN | NaN | 544.0 | 6905.0 | 17524.0 | 26150.0 | 12454.0 | 14826 | 47.1 | 0.4616 | 36472 | 34.3 | 17.161884 |
| 583 | Klamath County | OR | 05000US41035 | -121.646168 | 42.683761 | 66443 | 57742.0 | 134.0 | 702.0 | 3131.0 | 216.0 | 1793.0 | 2145.0 | 3873.0 | 14567.0 | 16319.0 | 8395.0 | 12438 | 42.1 | 0.4348 | 45604 | 29.6 | 21.283053 |
| 584 | Lane County | OR | 05000US41039 | -122.897678 | 43.928276 | 369519 | 324944.0 | 3663.0 | 9597.0 | 4162.0 | 928.0 | 6858.0 | 3970.0 | 16179.0 | 62062.0 | 96444.0 | 72815.0 | 68691 | 39.4 | 0.4588 | 47777 | 33.0 | 11.039049 |
| 585 | Linn County | OR | 05000US41043 | -122.543755 | 44.494824 | 122849 | 110734.0 | 595.0 | 1299.0 | 1248.0 | 74.0 | 4511.0 | 1361.0 | 5032.0 | 23747.0 | 35992.0 | 18493.0 | 15167 | 39.4 | 0.4535 | 51310 | 29.0 | 15.888091 |
| 586 | Marion County | OR | 05000US41047 | -122.576260 | 44.900898 | 336316 | 265422.0 | 4806.0 | 6959.0 | 2378.0 | 2402.0 | 23368.0 | 13462.0 | 14784.0 | 58814.0 | 79126.0 | 50042.0 | 43023 | 36.4 | 0.4143 | 56550 | 30.3 | 13.867126 |
| 587 | Multnomah County | OR | 05000US41051 | -122.417173 | 45.547693 | 799766 | 630308.0 | 45410.0 | 56252.0 | 5658.0 | 4647.0 | 16182.0 | 18592.0 | 27562.0 | 94851.0 | 171252.0 | 261887.0 | 113489 | 36.8 | 0.4743 | 62629 | 31.7 | 9.621313 |
| 588 | Polk County | OR | 05000US41053 | -123.397329 | 44.904395 | 81823 | 73654.0 | 358.0 | 1362.0 | 1905.0 | 369.0 | 1402.0 | 1443.0 | 3115.0 | 12616.0 | 19075.0 | 15523.0 | 9782 | 37.7 | 0.4222 | 52485 | 30.4 | 10.812195 |
| 589 | Umatilla County | OR | 05000US41059 | -118.733879 | 45.591200 | 76456 | 67399.0 | 573.0 | 266.0 | 1861.0 | 141.0 | 1998.0 | 2986.0 | 4459.0 | 14060.0 | 18499.0 | 7764.0 | 12240 | 35.4 | 0.4253 | 50171 | 28.1 | 18.127413 |
| 590 | Washington County | OR | 05000US41067 | -123.097615 | 45.553542 | 582779 | 439014.0 | 10846.0 | 58739.0 | 2252.0 | 2262.0 | 33702.0 | 12498.0 | 17947.0 | 70767.0 | 121926.0 | 170980.0 | 52590 | 36.6 | 0.4209 | 75634 | 28.9 | 6.808063 |
| 591 | Yamhill County | OR | 05000US41071 | -123.316117 | 45.248138 | 105035 | 91961.0 | 611.0 | 1804.0 | 1755.0 | 367.0 | 3317.0 | 1364.0 | 5514.0 | 19062.0 | 24713.0 | 18475.0 | 11150 | 38.2 | 0.4422 | 61596 | 27.0 | 14.129828 |
| 592 | Adams County | PA | 05000US42001 | -77.217730 | 39.869471 | 102180 | 94434.0 | 1057.0 | 426.0 | 65.0 | 0.0 | 3626.0 | 1722.0 | 6042.0 | 29115.0 | 17486.0 | 15489.0 | 9338 | 44.2 | 0.3985 | 59300 | 29.1 | 17.277377 |
| 593 | Allegheny County | PA | 05000US42003 | -79.980920 | 40.468920 | 1225365 | 982979.0 | 154858.0 | 43283.0 | 975.0 | 443.0 | 4933.0 | 8884.0 | 34875.0 | 241290.0 | 225945.0 | 366908.0 | 138887 | 40.6 | 0.4769 | 56140 | 27.2 | 14.349432 |
| 594 | Armstrong County | PA | 05000US42005 | -79.464128 | 40.812379 | 66486 | NaN | NaN | NaN | NaN | NaN | NaN | 371.0 | 4451.0 | 23231.0 | 12443.0 | 7965.0 | 9309 | 46.5 | 0.4284 | 47398 | 28.8 | 23.719594 |
| 595 | Beaver County | PA | 05000US42007 | -80.350721 | 40.684140 | 167429 | 151718.0 | 9238.0 | 551.0 | 91.0 | 194.0 | 779.0 | 1088.0 | 7335.0 | 44875.0 | 37106.0 | 31505.0 | 13561 | 44.9 | 0.4167 | 55221 | 24.1 | 19.678540 |
| 596 | Berks County | PA | 05000US42011 | -75.926860 | 40.413956 | 414812 | 343554.0 | 18603.0 | 5504.0 | 4478.0 | 0.0 | 14887.0 | 8449.0 | 21719.0 | 104558.0 | 67703.0 | 70120.0 | 54476 | 40.1 | 0.4386 | 59286 | 30.5 | 14.550338 |
| 597 | Blair County | PA | 05000US42013 | -78.310640 | 40.497926 | 124650 | NaN | NaN | NaN | NaN | NaN | NaN | 839.0 | 5909.0 | 42849.0 | 22536.0 | 16344.0 | 15689 | 43.8 | 0.4395 | 43443 | 27.3 | 22.297377 |
| 598 | Bucks County | PA | 05000US42017 | -75.107060 | 40.336887 | 626399 | 551410.0 | 22210.0 | 30721.0 | 912.0 | 0.0 | 7193.0 | 6081.0 | 19560.0 | 131824.0 | 106286.0 | 178760.0 | 40818 | 43.7 | 0.4540 | 79936 | 29.8 | 10.470003 |
| 599 | Butler County | PA | 05000US42019 | -79.918960 | 40.913834 | 186847 | 179023.0 | 2329.0 | 2478.0 | 177.0 | 0.0 | 727.0 | 1044.0 | 4676.0 | 42321.0 | 35499.0 | 48338.0 | 11214 | 43.3 | 0.4364 | 66426 | 25.7 | 12.449650 |
| 600 | Cambria County | PA | 05000US42021 | -78.715284 | 40.494127 | 134732 | 126107.0 | 3266.0 | 515.0 | 13.0 | 175.0 | 527.0 | 1150.0 | 5442.0 | 41827.0 | 25163.0 | 21300.0 | 20303 | 45.1 | 0.4288 | 44100 | 28.5 | 23.223429 |
| 601 | Carbon County | PA | 05000US42025 | -75.709428 | 40.917833 | 63594 | NaN | NaN | NaN | NaN | NaN | NaN | 456.0 | 3805.0 | 21864.0 | 11999.0 | 8142.0 | 9152 | 46.2 | 0.4038 | 51676 | 24.3 | 17.097668 |
| 602 | Centre County | PA | 05000US42027 | -77.847830 | 40.909160 | 161464 | 142112.0 | 5949.0 | 8766.0 | 115.0 | 80.0 | 437.0 | 975.0 | 2969.0 | 29060.0 | 17544.0 | 48006.0 | 25619 | 31.7 | 0.4812 | 60266 | 34.9 | 8.895663 |
| 603 | Chester County | PA | 05000US42029 | -75.749732 | 39.973965 | 516312 | 440376.0 | 29721.0 | 25578.0 | 506.0 | 254.0 | 6783.0 | 6364.0 | 14195.0 | 76114.0 | 71295.0 | 178576.0 | 36998 | 40.3 | 0.4733 | 92407 | 28.7 | 9.710024 |
| 604 | Clearfield County | PA | 05000US42033 | -78.473749 | 41.000249 | 80596 | NaN | NaN | NaN | NaN | NaN | NaN | 1072.0 | 5761.0 | 29274.0 | 13430.0 | 8363.0 | 9887 | 44.8 | 0.4168 | 47352 | 26.4 | 24.266199 |
| 605 | Columbia County | PA | 05000US42037 | -76.404260 | 41.045517 | 66420 | NaN | NaN | NaN | NaN | NaN | NaN | 413.0 | 3647.0 | 17759.0 | 11288.0 | 10010.0 | 7755 | 40.6 | 0.4383 | 49186 | 26.5 | 19.644944 |
| 606 | Crawford County | PA | 05000US42039 | -80.107811 | 41.686840 | 86257 | 82613.0 | 1507.0 | 314.0 | 14.0 | 51.0 | 192.0 | 392.0 | 4260.0 | 27727.0 | 12691.0 | 13248.0 | 11286 | 42.7 | 0.4440 | 45410 | 22.4 | 19.596525 |
| 607 | Cumberland County | PA | 05000US42041 | -77.263440 | 40.164782 | 248506 | 218262.0 | 10293.0 | 10925.0 | 171.0 | 0.0 | 4149.0 | 1577.0 | 11027.0 | 54395.0 | 42258.0 | 62762.0 | 18153 | 40.2 | 0.4188 | 63530 | 25.6 | 13.261305 |
| 608 | Dauphin County | PA | 05000US42043 | -76.792634 | 40.412565 | 273707 | 193791.0 | 52861.0 | 11959.0 | 918.0 | 0.0 | 7929.0 | 2983.0 | 12797.0 | 61179.0 | 48024.0 | 61600.0 | 29872 | 39.6 | 0.4449 | 60331 | 27.3 | 13.717419 |
| 609 | Delaware County | PA | 05000US42045 | -75.398786 | 39.916670 | 563402 | 389846.0 | 120082.0 | 32804.0 | 1165.0 | 0.0 | 6352.0 | 5832.0 | 18346.0 | 118131.0 | 89724.0 | 146227.0 | 59097 | 39.0 | 0.4798 | 67950 | 30.3 | 11.750687 |
| 610 | Erie County | PA | 05000US42049 | -80.096386 | 42.117952 | 276207 | 241733.0 | 20144.0 | 4980.0 | 613.0 | 201.0 | 1903.0 | 2864.0 | 10557.0 | 77664.0 | 43235.0 | 52826.0 | 41799 | 39.2 | 0.4442 | 48964 | 28.1 | 18.505082 |
| 611 | Fayette County | PA | 05000US42051 | -79.644586 | 39.914115 | 132733 | 122730.0 | 5313.0 | 320.0 | 63.0 | 56.0 | 589.0 | 1096.0 | 9743.0 | 48650.0 | 20901.0 | 15813.0 | 22414 | 44.8 | 0.4533 | 43140 | 28.6 | 22.714398 |
| 612 | Franklin County | PA | 05000US42055 | -77.724485 | 39.926686 | 153851 | NaN | NaN | NaN | NaN | NaN | NaN | 997.0 | 9281.0 | 45105.0 | 25800.0 | 23048.0 | 12115 | 41.8 | 0.4260 | 60559 | 25.2 | 19.230642 |
| 613 | Indiana County | PA | 05000US42063 | -79.087545 | 40.651432 | 86364 | NaN | NaN | NaN | NaN | NaN | NaN | 250.0 | 4163.0 | 24710.0 | 13368.0 | 12369.0 | 17239 | 39.7 | 0.4606 | 42962 | 34.5 | 24.992813 |
| 614 | Lackawanna County | PA | 05000US42069 | -75.609587 | 41.440250 | 211321 | 193832.0 | 5870.0 | 5204.0 | 214.0 | 58.0 | 1246.0 | 3323.0 | 9262.0 | 55829.0 | 38757.0 | 40337.0 | 28922 | 41.7 | 0.4634 | 47475 | 27.6 | 21.166317 |
| 615 | Lancaster County | PA | 05000US42071 | -76.250198 | 40.041992 | 538500 | 481798.0 | 23427.0 | 10923.0 | 431.0 | 53.0 | 9219.0 | 7737.0 | 31564.0 | 132943.0 | 82824.0 | 90202.0 | 58032 | 38.2 | 0.4331 | 61335 | 28.3 | 15.485730 |
| 616 | Lawrence County | PA | 05000US42073 | -80.334446 | 40.992735 | 87294 | NaN | NaN | NaN | NaN | NaN | NaN | 847.0 | 4291.0 | 26940.0 | 15194.0 | 14337.0 | 10867 | 45.0 | 0.4837 | 46918 | 28.3 | 22.673657 |
| 617 | Lebanon County | PA | 05000US42075 | -76.458009 | 40.367344 | 138863 | 120894.0 | 3650.0 | 2078.0 | 129.0 | 0.0 | 9250.0 | 1101.0 | 7950.0 | 42667.0 | 21504.0 | 19393.0 | 12589 | 41.2 | 0.4073 | 57248 | 27.3 | 15.976771 |
| 618 | Lehigh County | PA | 05000US42077 | -75.590627 | 40.614241 | 363147 | 286600.0 | 26645.0 | 11587.0 | 358.0 | 0.0 | 26967.0 | 6338.0 | 15573.0 | 84283.0 | 64008.0 | 72981.0 | 51444 | 39.0 | 0.4692 | 60498 | 31.5 | 14.247229 |
| 619 | Luzerne County | PA | 05000US42079 | -75.976034 | 41.172787 | 316383 | 285321.0 | 13937.0 | 3756.0 | 452.0 | 77.0 | 7116.0 | 3344.0 | 16878.0 | 88057.0 | 61987.0 | 52090.0 | 44974 | 43.1 | 0.4641 | 46580 | 27.9 | 22.619604 |
| 620 | Lycoming County | PA | 05000US42081 | -77.055253 | 41.343624 | 115248 | NaN | NaN | NaN | NaN | NaN | NaN | 589.0 | 5310.0 | 30222.0 | 25842.0 | 17082.0 | 16487 | 40.6 | 0.4148 | 49052 | 31.4 | 15.883580 |
| 621 | Mercer County | PA | 05000US42085 | -80.252786 | 41.300014 | 112913 | NaN | NaN | NaN | NaN | NaN | NaN | 960.0 | 5498.0 | 34099.0 | 20367.0 | 17607.0 | 17715 | 45.1 | 0.4262 | 49890 | 25.7 | 16.912119 |
| 622 | Monroe County | PA | 05000US42089 | -75.329037 | 41.056233 | 166098 | 124741.0 | 23137.0 | 3492.0 | 257.0 | 191.0 | 8899.0 | 2353.0 | 7623.0 | 40561.0 | 32527.0 | 28986.0 | 18575 | 41.9 | 0.4177 | 60095 | 25.7 | 14.812164 |
| 623 | Montgomery County | PA | 05000US42091 | -75.370201 | 40.209999 | 821725 | 654626.0 | 73076.0 | 60749.0 | 1230.0 | 295.0 | 9751.0 | 7024.0 | 22217.0 | 139353.0 | 127461.0 | 277024.0 | 49570 | 41.4 | 0.4623 | 84113 | 29.0 | 9.205401 |
| 624 | Northampton County | PA | 05000US42095 | -75.307447 | 40.752791 | 302294 | 262942.0 | 15889.0 | 8454.0 | 459.0 | 0.0 | 4759.0 | 4279.0 | 12630.0 | 74744.0 | 55902.0 | 60921.0 | 24583 | 41.6 | 0.4176 | 66438 | 28.2 | 14.046170 |
| 625 | Northumberland County | PA | 05000US42097 | -76.709877 | 40.851524 | 92541 | 87742.0 | 2471.0 | 432.0 | 127.0 | 0.0 | 665.0 | 1203.0 | 6542.0 | 34012.0 | 13107.0 | 11947.0 | 13035 | 44.7 | 0.4201 | 47736 | 23.5 | 26.245472 |
| 626 | Philadelphia County | PA | 05000US42101 | -75.133346 | 40.009375 | 1567872 | 634111.0 | 661032.0 | 110733.0 | 5521.0 | 480.0 | 104892.0 | 42857.0 | 115831.0 | 336164.0 | 241777.0 | 302678.0 | 391653 | 34.1 | 0.5153 | 41449 | 32.2 | 21.933455 |
| 627 | Schuylkill County | PA | 05000US42107 | -76.217800 | 40.703690 | 143573 | 133893.0 | 4370.0 | 829.0 | 288.0 | 0.0 | 2261.0 | 1711.0 | 9375.0 | 49938.0 | 26971.0 | 16565.0 | 17316 | 44.4 | 0.4072 | 50684 | 28.7 | 22.899792 |
| 628 | Somerset County | PA | 05000US42111 | -79.028486 | 39.981297 | 75061 | NaN | NaN | NaN | NaN | NaN | NaN | 1292.0 | 3944.0 | 26815.0 | 13219.0 | 8968.0 | 9368 | 45.7 | 0.4300 | 43871 | 24.5 | 26.403782 |
| 629 | Washington County | PA | 05000US42125 | -80.252132 | 40.200005 | 207981 | 194397.0 | 6242.0 | 1981.0 | 271.0 | 0.0 | 1153.0 | 2047.0 | 7571.0 | 58011.0 | 36170.0 | 43932.0 | 18836 | 44.6 | 0.4532 | 57998 | 27.2 | 14.453120 |
| 630 | Westmoreland County | PA | 05000US42129 | -79.466688 | 40.311068 | 355458 | 336698.0 | 9544.0 | 3691.0 | 75.0 | 34.0 | 797.0 | 2414.0 | 10241.0 | 97854.0 | 74883.0 | 73462.0 | 33545 | 46.7 | 0.4325 | 56722 | 27.0 | 17.266828 |
| 631 | York County | PA | 05000US42133 | -76.728446 | 39.921839 | 443744 | 391834.0 | 25561.0 | 6918.0 | 943.0 | 231.0 | 7853.0 | 4262.0 | 22656.0 | 126295.0 | 75680.0 | 74615.0 | 43053 | 41.2 | 0.4015 | 62462 | 29.1 | 15.928422 |
| 632 | Kent County | RI | 05000US44003 | -71.576313 | 41.677750 | 164614 | 151589.0 | 2738.0 | 3675.0 | 196.0 | 128.0 | 1900.0 | 1923.0 | 6602.0 | 35352.0 | 37410.0 | 39148.0 | 15376 | 43.5 | 0.4716 | 63101 | 29.2 | 12.038909 |
| 633 | Newport County | RI | 05000US44005 | -71.284063 | 41.502732 | 82784 | NaN | NaN | NaN | NaN | NaN | NaN | 659.0 | 1849.0 | 13102.0 | 16560.0 | 27231.0 | 5595 | 45.6 | 0.4561 | 73596 | 27.6 | 11.485832 |
| 634 | Providence County | RI | 05000US44007 | -71.578242 | 41.870488 | 633673 | 460377.0 | 58806.0 | 26713.0 | 4643.0 | 572.0 | 57811.0 | 22819.0 | 31705.0 | 129921.0 | 111879.0 | 127085.0 | 93332 | 37.5 | 0.4775 | 52369 | 29.8 | 17.571099 |
| 635 | Washington County | RI | 05000US44009 | -71.617612 | 41.401162 | 126288 | NaN | NaN | NaN | NaN | NaN | NaN | 626.0 | 3185.0 | 20409.0 | 21610.0 | 40283.0 | 12477 | 45.1 | 0.4609 | 77813 | 30.6 | 10.576159 |
| 636 | Aiken County | SC | 05000US45003 | -81.633870 | 33.549317 | 167458 | NaN | NaN | NaN | NaN | NaN | NaN | 2839.0 | 11573.0 | 38262.0 | 34279.0 | 27598.0 | 29696 | 40.8 | 0.4510 | 45945 | 28.3 | 18.946533 |
| 637 | Anderson County | SC | 05000US45007 | -82.638086 | 34.519549 | 196569 | 156660.0 | 32543.0 | 1935.0 | 309.0 | 0.0 | 1944.0 | 3596.0 | 14755.0 | 45300.0 | 39308.0 | 28454.0 | 31011 | 40.4 | 0.4554 | 45090 | 27.3 | 21.941616 |
| 638 | Beaufort County | SC | 05000US45013 | -80.689320 | 32.358147 | 183149 | 138177.0 | 33588.0 | 2367.0 | 659.0 | 106.0 | 4005.0 | 1269.0 | 4743.0 | 29958.0 | 39003.0 | 54567.0 | 16522 | 44.9 | 0.4731 | 65919 | 30.7 | 13.504681 |
| 639 | Berkeley County | SC | 05000US45015 | -79.953655 | 33.207700 | 210898 | 141762.0 | 51487.0 | 5728.0 | 644.0 | 0.0 | 5460.0 | 3017.0 | 12603.0 | 38064.0 | 48211.0 | 35057.0 | 25606 | 36.4 | 0.4266 | 59153 | 28.4 | 15.377251 |
| 640 | Charleston County | SC | 05000US45019 | -79.942480 | 32.800458 | 396484 | 271106.0 | 109189.0 | 6001.0 | 626.0 | 0.0 | 2356.0 | 4438.0 | 20976.0 | 62118.0 | 73572.0 | 117081.0 | 58935 | 37.1 | 0.5249 | 56827 | 31.3 | 14.295187 |
| 641 | Darlington County | SC | 05000US45031 | -79.962115 | 34.332185 | 67234 | NaN | NaN | NaN | NaN | NaN | NaN | 2230.0 | 6718.0 | 15283.0 | 12918.0 | 7957.0 | 13028 | 41.2 | 0.4570 | 36841 | 27.7 | 28.896004 |
| 642 | Dorchester County | SC | 05000US45035 | -80.404697 | 33.082186 | 153773 | 103108.0 | 37763.0 | 2803.0 | 1078.0 | 0.0 | 3173.0 | 2483.0 | 8136.0 | 26497.0 | 33614.0 | 29784.0 | 12032 | 36.3 | 0.4146 | 57637 | 30.5 | 12.414142 |
| 643 | Florence County | SC | 05000US45041 | -79.710233 | 34.028535 | 138742 | NaN | NaN | NaN | NaN | NaN | NaN | 3608.0 | 10096.0 | 28824.0 | 28327.0 | 20909.0 | 24362 | 39.0 | 0.4606 | 46524 | 28.9 | 26.740781 |
| 644 | Greenville County | SC | 05000US45045 | -82.372077 | 34.892645 | 498766 | 372089.0 | 91056.0 | 11379.0 | 1844.0 | 439.0 | 11693.0 | 8208.0 | 28320.0 | 77733.0 | 103316.0 | 115302.0 | 52236 | 38.1 | 0.4649 | 55342 | 29.1 | 16.030262 |
| 645 | Greenwood County | SC | 05000US45047 | -82.127876 | 34.155796 | 70133 | NaN | NaN | NaN | NaN | NaN | NaN | 893.0 | 4751.0 | 13719.0 | 14492.0 | 10921.0 | 17166 | 37.8 | 0.4762 | 38200 | 31.1 | 28.351433 |
| 646 | Horry County | SC | 05000US45051 | -78.976675 | 33.909269 | 322342 | 260680.0 | 44012.0 | 4425.0 | 1727.0 | 192.0 | 6660.0 | 4842.0 | 16264.0 | 75308.0 | 84171.0 | 53267.0 | 49075 | 45.3 | 0.4465 | 45621 | 32.1 | 12.204329 |
| 647 | Lancaster County | SC | 05000US45057 | -80.703688 | 34.686818 | 89594 | NaN | NaN | NaN | NaN | NaN | NaN | 373.0 | 6082.0 | 20997.0 | 17446.0 | 16927.0 | 11154 | 42.6 | 0.4798 | 56216 | 28.2 | 18.320564 |
| 648 | Laurens County | SC | 05000US45059 | -82.005657 | 34.483477 | 66777 | 47288.0 | 14837.0 | 981.0 | 128.0 | 471.0 | 1017.0 | 1386.0 | 5986.0 | 14229.0 | 15488.0 | 6333.0 | 11536 | 39.2 | 0.4245 | 44038 | 26.4 | 23.975005 |
| 649 | Lexington County | SC | 05000US45063 | -81.272853 | 33.892553 | 286196 | 227447.0 | 41478.0 | 4965.0 | 627.0 | 111.0 | 3819.0 | 4893.0 | 12763.0 | 58443.0 | 57904.0 | 60145.0 | 34038 | 38.9 | 0.4392 | 57382 | 30.2 | 14.631489 |
| 650 | Oconee County | SC | 05000US45073 | -83.061522 | 34.748759 | 76355 | NaN | NaN | NaN | NaN | NaN | NaN | 1140.0 | 5210.0 | 16345.0 | 16931.0 | 13332.0 | 11691 | 44.7 | 0.4735 | 43743 | 31.4 | 23.169725 |
| 651 | Orangeburg County | SC | 05000US45075 | -80.802913 | 33.436135 | 87903 | NaN | NaN | NaN | NaN | NaN | NaN | 1404.0 | 5864.0 | 18633.0 | 20928.0 | 10586.0 | 17341 | 39.4 | 0.5016 | 32450 | 33.3 | 28.207121 |
| 652 | Pickens County | SC | 05000US45077 | -82.725368 | 34.887361 | 122863 | NaN | NaN | NaN | NaN | NaN | NaN | 2309.0 | 8432.0 | 20668.0 | 23150.0 | 21278.0 | 17108 | 36.3 | 0.4761 | 45779 | 27.7 | 18.833901 |
| 653 | Richland County | SC | 05000US45079 | -80.896566 | 34.029783 | 409549 | 186850.0 | 190887.0 | 10198.0 | 695.0 | 545.0 | 8467.0 | 5603.0 | 15745.0 | 56513.0 | 75631.0 | 99193.0 | 63266 | 33.4 | 0.4669 | 52030 | 29.6 | 15.514161 |
| 654 | Spartanburg County | SC | 05000US45083 | -81.991053 | 34.933239 | 301463 | 221226.0 | 61262.0 | 6514.0 | 197.0 | 30.0 | 4830.0 | 6293.0 | 20187.0 | 63418.0 | 63411.0 | 44975.0 | 47158 | 38.1 | 0.4635 | 47371 | 27.0 | 18.848435 |
| 655 | Sumter County | SC | 05000US45085 | -80.382472 | 33.916046 | 107396 | NaN | NaN | NaN | NaN | NaN | NaN | 1718.0 | 6834.0 | 22046.0 | 24260.0 | 13588.0 | 22220 | 36.1 | 0.4923 | 40614 | 31.6 | 23.033857 |
| 656 | York County | SC | 05000US45091 | -81.183188 | 34.970190 | 258526 | 191332.0 | 48293.0 | 4854.0 | 2027.0 | 115.0 | 5796.0 | 3279.0 | 11294.0 | 44709.0 | 58358.0 | 51750.0 | 27864 | 38.7 | 0.4416 | 60767 | 27.0 | 10.918392 |
| 657 | Minnehaha County | SD | 05000US46099 | -96.795726 | 43.667472 | 187318 | 161240.0 | 9385.0 | 4477.0 | 3948.0 | 67.0 | 2887.0 | 2499.0 | 5856.0 | 31106.0 | 40397.0 | 40891.0 | 18693 | 34.9 | 0.4301 | 60038 | 26.6 | 10.524604 |
| 658 | Pennington County | SD | 05000US46103 | -102.823802 | 44.002349 | 109372 | 90775.0 | 1426.0 | 1444.0 | 10308.0 | 14.0 | 254.0 | 996.0 | 4693.0 | 19493.0 | 26098.0 | 22448.0 | 17125 | 38.3 | 0.4707 | 50950 | 30.2 | 12.042555 |
| 659 | Anderson County | TN | 05000US47001 | -84.195418 | 36.116731 | 75936 | NaN | NaN | NaN | NaN | NaN | NaN | 1083.0 | 3810.0 | 17399.0 | 17801.0 | 12554.0 | 9652 | 43.4 | 0.4470 | 46055 | 25.5 | 15.239194 |
| 660 | Blount County | TN | 05000US47009 | -83.922973 | 35.688185 | 128670 | NaN | NaN | NaN | NaN | NaN | NaN | 1306.0 | 7035.0 | 31089.0 | 29563.0 | 21046.0 | 13234 | 43.7 | 0.4504 | 51183 | 24.3 | 21.394347 |
| 661 | Bradley County | TN | 05000US47011 | -84.859414 | 35.153914 | 104490 | NaN | NaN | NaN | NaN | NaN | NaN | 1328.0 | 6209.0 | 24916.0 | 20232.0 | 16509.0 | 13560 | 39.2 | 0.4289 | 44853 | 24.7 | 23.448784 |
| 662 | Davidson County | TN | 05000US47037 | -86.784790 | 36.169129 | 684410 | 442201.0 | 187329.0 | 25891.0 | 1398.0 | 605.0 | 10795.0 | 13024.0 | 38684.0 | 104703.0 | 119452.0 | 186581.0 | 98479 | 34.2 | 0.4806 | 54855 | 28.5 | 12.938039 |
| 663 | Greene County | TN | 05000US47059 | -82.847746 | 36.178998 | 68615 | NaN | NaN | NaN | NaN | NaN | NaN | 1767.0 | 4084.0 | 21737.0 | 12545.0 | 8056.0 | 11161 | 44.9 | 0.5066 | 41109 | 32.6 | 25.957983 |
| 664 | Hamilton County | TN | 05000US47065 | -85.202295 | 35.159186 | 357738 | 271063.0 | 70591.0 | 6865.0 | 499.0 | 0.0 | 924.0 | 5625.0 | 18017.0 | 73283.0 | 71836.0 | 76657.0 | 45768 | 39.0 | 0.4952 | 47898 | 31.2 | 19.411954 |
| 665 | Knox County | TN | 05000US47093 | -83.937721 | 35.992727 | 456132 | 390124.0 | 40267.0 | 10650.0 | 1780.0 | 261.0 | 4973.0 | 4742.0 | 19300.0 | 73373.0 | 86294.0 | 117189.0 | 66094 | 37.3 | 0.4752 | 52102 | 28.7 | 13.874131 |
| 666 | Madison County | TN | 05000US47113 | -88.833424 | 35.606056 | 97663 | NaN | NaN | NaN | NaN | NaN | NaN | 830.0 | 5564.0 | 26173.0 | 17193.0 | 13555.0 | 18688 | 38.0 | 0.4816 | 41791 | 32.1 | 20.116821 |
| 667 | Maury County | TN | 05000US47119 | -87.077763 | 35.615696 | 89981 | NaN | NaN | NaN | NaN | NaN | NaN | 615.0 | 3621.0 | 21101.0 | 21631.0 | 13733.0 | 8894 | 39.9 | 0.4123 | 50591 | 26.7 | 17.872100 |
| 668 | Montgomery County | TN | 05000US47125 | -87.380887 | 36.500353 | 195734 | 140854.0 | 37819.0 | 5439.0 | 1383.0 | 1446.0 | 2057.0 | 1938.0 | 7122.0 | 35998.0 | 43735.0 | 31230.0 | 26026 | 30.8 | 0.3930 | 56112 | 28.0 | 12.921486 |
| 669 | Putnam County | TN | 05000US47141 | -85.496928 | 36.140807 | 75931 | NaN | NaN | NaN | NaN | NaN | NaN | 1546.0 | 3834.0 | 18848.0 | 9835.0 | 13836.0 | 15077 | 36.7 | 0.5067 | 37437 | 32.0 | 20.958428 |
| 670 | Robertson County | TN | 05000US47147 | -86.869377 | 36.527530 | 69165 | NaN | NaN | NaN | NaN | NaN | NaN | 1045.0 | 4126.0 | 17855.0 | 13342.0 | 9311.0 | 5705 | 37.9 | 0.4370 | 60423 | 26.9 | 20.286934 |
| 671 | Rutherford County | TN | 05000US47149 | -86.417213 | 35.843369 | 308251 | 239190.0 | 43917.0 | 10482.0 | 755.0 | 0.0 | 3974.0 | 2641.0 | 8966.0 | 56683.0 | 60483.0 | 61019.0 | 31360 | 33.1 | 0.4021 | 61157 | 28.4 | 10.330649 |
| 672 | Sevier County | TN | 05000US47155 | -83.521955 | 35.791284 | 96673 | NaN | NaN | NaN | NaN | NaN | NaN | 1441.0 | 5759.0 | 26211.0 | 20156.0 | 12441.0 | 15180 | 41.3 | 0.4064 | 45609 | 28.2 | 16.554934 |
| 673 | Shelby County | TN | 05000US47157 | -89.895397 | 35.183794 | 934603 | 360410.0 | 499045.0 | 22771.0 | 2855.0 | 165.0 | 31109.0 | 16739.0 | 45725.0 | 156620.0 | 193404.0 | 187338.0 | 190483 | 35.5 | 0.5108 | 47690 | 31.7 | 22.925513 |
| 674 | Sullivan County | TN | 05000US47163 | -82.299397 | 36.510212 | 156667 | 147950.0 | 2912.0 | 664.0 | 184.0 | 418.0 | 1169.0 | 2198.0 | 10487.0 | 42430.0 | 32784.0 | 22638.0 | 25022 | 45.0 | 0.4491 | 42859 | 26.9 | 19.868817 |
| 675 | Sumner County | TN | 05000US47165 | -86.458517 | 36.470015 | 180063 | 160406.0 | 13146.0 | 2300.0 | 336.0 | 60.0 | 412.0 | 2119.0 | 6744.0 | 39622.0 | 38295.0 | 33146.0 | 17004 | 40.1 | 0.4346 | 60503 | 29.4 | 15.667475 |
| 676 | Washington County | TN | 05000US47179 | -82.495037 | 36.295665 | 127440 | NaN | NaN | NaN | NaN | NaN | NaN | 1333.0 | 7489.0 | 26517.0 | 22159.0 | 27686.0 | 17802 | 39.9 | 0.4462 | 46276 | 27.8 | 11.935796 |
| 677 | Williamson County | TN | 05000US47187 | -86.896958 | 35.894972 | 219107 | 194634.0 | 9008.0 | 9027.0 | 251.0 | 515.0 | 1127.0 | 2869.0 | 5025.0 | 19379.0 | 34568.0 | 79559.0 | 12861 | 39.0 | 0.4485 | 106054 | 27.0 | 5.007384 |
| 678 | Wilson County | TN | 05000US47189 | -86.290210 | 36.148476 | 132781 | NaN | NaN | NaN | NaN | NaN | NaN | 1333.0 | 6302.0 | 24004.0 | 31189.0 | 26568.0 | 10184 | 41.1 | 0.3937 | 71153 | 22.7 | 11.708974 |
| 679 | Angelina County | TX | 05000US48005 | -94.611056 | 31.251951 | 87791 | NaN | NaN | NaN | NaN | NaN | NaN | 4296.0 | 7057.0 | 16523.0 | 18850.0 | 8976.0 | 16908 | 37.6 | 0.4566 | 41161 | 26.9 | 16.481298 |
| 680 | Bastrop County | TX | 05000US48021 | -97.311859 | 30.103128 | 82733 | NaN | NaN | NaN | NaN | NaN | NaN | 3337.0 | 4847.0 | 14816.0 | 19002.0 | 10989.0 | 10251 | 38.7 | 0.4659 | 56508 | 33.4 | 19.114533 |
| 681 | Bell County | TX | 05000US48027 | -97.481921 | 31.042110 | 340411 | 217524.0 | 82612.0 | 9696.0 | 1945.0 | 2515.0 | 6984.0 | 5340.0 | 12673.0 | 52921.0 | 86780.0 | 45947.0 | 41475 | 31.0 | 0.4323 | 52275 | 27.4 | 12.921615 |
| 682 | Bexar County | TX | 05000US48029 | -98.520146 | 29.448671 | 1928680 | 1520769.0 | 151997.0 | 56054.0 | 14260.0 | 1282.0 | 129637.0 | 72858.0 | 117501.0 | 309505.0 | 361575.0 | 341692.0 | 307296 | 33.5 | 0.4595 | 53210 | 29.5 | 16.275257 |
| 683 | Bowie County | TX | 05000US48037 | -94.422375 | 33.446051 | 93860 | 65905.0 | 22872.0 | 1265.0 | 298.0 | 314.0 | 680.0 | 1001.0 | 5329.0 | 23452.0 | 19661.0 | 13412.0 | 17204 | 37.0 | 0.5029 | 45997 | 27.4 | 30.132300 |
| 684 | Brazoria County | TX | 05000US48039 | -95.434647 | 29.167817 | 354195 | 264794.0 | 45897.0 | 23012.0 | 3592.0 | 13.0 | 9487.0 | 7418.0 | 15921.0 | 64475.0 | 74223.0 | 63307.0 | 32885 | 36.0 | 0.4102 | 74799 | 24.9 | 11.236003 |
| 685 | Brazos County | TX | 05000US48041 | -96.302389 | 30.656725 | 220417 | 167258.0 | 22640.0 | 14050.0 | 1067.0 | 107.0 | 7067.0 | 6432.0 | 7746.0 | 23127.0 | 31998.0 | 45155.0 | 53402 | 26.1 | 0.5372 | 41559 | 40.8 | 10.076400 |
| 686 | Cameron County | TX | 05000US48061 | -97.478958 | 26.102923 | 422135 | NaN | NaN | NaN | NaN | NaN | NaN | 35111.0 | 32579.0 | 66538.0 | 57779.0 | 41850.0 | 122269 | 31.5 | 0.4899 | 37061 | 32.6 | 31.026814 |
| 687 | Collin County | TX | 05000US48085 | -96.578153 | 33.193885 | 939585 | 661559.0 | 87766.0 | 132486.0 | 3583.0 | 822.0 | 23075.0 | 15011.0 | 19320.0 | 92547.0 | 163601.0 | 317049.0 | 59604 | 36.5 | 0.4236 | 89638 | 26.1 | 6.667981 |
| 688 | Comal County | TX | 05000US48091 | -98.255201 | 29.803019 | 134788 | NaN | NaN | NaN | NaN | NaN | NaN | 1419.0 | 3524.0 | 24111.0 | 28781.0 | 33732.0 | 11575 | 42.6 | 0.4417 | 77425 | 28.2 | 10.307794 |
| 689 | Coryell County | TX | 05000US48099 | -97.798022 | 31.391177 | 74686 | 53879.0 | 7911.0 | 1656.0 | 960.0 | 558.0 | 788.0 | 2146.0 | 2957.0 | 13314.0 | 20447.0 | 7567.0 | 8270 | 31.7 | 0.4180 | 51125 | 26.2 | 11.767040 |
| 690 | Dallas County | TX | 05000US48113 | -96.778424 | 32.766987 | 2574984 | 1591187.0 | 582365.0 | 157592.0 | 6506.0 | 1393.0 | 159620.0 | 151883.0 | 159910.0 | 360041.0 | 424294.0 | 498808.0 | 414218 | 33.3 | 0.4957 | 54399 | 28.1 | 16.923969 |
| 691 | Denton County | TX | 05000US48121 | -97.119046 | 33.205005 | 806180 | 599270.0 | 74199.0 | 63864.0 | 3992.0 | 602.0 | 32029.0 | 15307.0 | 21312.0 | 92430.0 | 157526.0 | 233571.0 | 67887 | 35.1 | 0.4413 | 80613 | 28.4 | 7.592459 |
| 692 | Ector County | TX | 05000US48135 | -102.542507 | 31.865301 | 157462 | 134245.0 | 7643.0 | 1809.0 | 1069.0 | 0.0 | 8735.0 | 5132.0 | 14343.0 | 29241.0 | 27143.0 | 14331.0 | 19680 | 30.4 | 0.5128 | 53254 | 26.5 | 16.053086 |
| 693 | Ellis County | TX | 05000US48139 | -96.798336 | 32.347279 | 168499 | 141134.0 | 16525.0 | 1390.0 | 1104.0 | 83.0 | 2379.0 | 4745.0 | 10202.0 | 29355.0 | 38139.0 | 23450.0 | 15427 | 36.1 | 0.4152 | 70210 | 28.9 | 8.417587 |
| 694 | El Paso County | TX | 05000US48141 | -106.241391 | 31.766403 | 837918 | 674348.0 | 30034.0 | 11107.0 | 5360.0 | 299.0 | 95668.0 | 56620.0 | 47755.0 | 118897.0 | 161938.0 | 112402.0 | 187442 | 32.1 | 0.4598 | 42165 | 30.4 | 17.988152 |
| 695 | Fort Bend County | TX | 05000US48157 | -95.771015 | 29.526602 | 741237 | 388931.0 | 152115.0 | 146796.0 | 3077.0 | 93.0 | 34405.0 | 15009.0 | 23954.0 | 90429.0 | 127278.0 | 213489.0 | 63468 | 36.0 | 0.4482 | 90680 | 29.3 | 7.021958 |
| 696 | Galveston County | TX | 05000US48167 | -94.894865 | 29.228706 | 329431 | 258194.0 | 43453.0 | 11418.0 | 2556.0 | 87.0 | 7317.0 | 7357.0 | 16515.0 | 52487.0 | 76601.0 | 64075.0 | 41712 | 37.1 | 0.4692 | 64939 | 29.7 | 13.365526 |
| 697 | Grayson County | TX | 05000US48181 | -96.675699 | 33.624508 | 128235 | 111018.0 | 5527.0 | 1826.0 | 1352.0 | 16.0 | 3119.0 | 1875.0 | 6819.0 | 24262.0 | 35853.0 | 16843.0 | 15741 | 40.0 | 0.4290 | 52095 | 25.4 | 20.776493 |
| 698 | Gregg County | TX | 05000US48183 | -94.816276 | 32.486397 | 123745 | 92247.0 | 25793.0 | 1883.0 | 534.0 | 0.0 | 1037.0 | 4663.0 | 9549.0 | 22391.0 | 24801.0 | 17023.0 | 21654 | 35.5 | 0.4727 | 44219 | 31.0 | 17.244810 |
| 699 | Guadalupe County | TX | 05000US48187 | -97.949027 | 29.583208 | 155265 | 106494.0 | 11011.0 | 3061.0 | 187.0 | 1657.0 | 28146.0 | 3426.0 | 6139.0 | 32511.0 | 29021.0 | 27689.0 | 15983 | 37.0 | 0.4076 | 68157 | 27.6 | 12.061230 |
| 700 | Harris County | TX | 05000US48201 | -95.393037 | 29.857273 | 4589928 | 2880994.0 | 874306.0 | 317740.0 | 15178.0 | 3839.0 | 392701.0 | 251441.0 | 237148.0 | 686011.0 | 771762.0 | 903838.0 | 755013 | 33.3 | 0.4986 | 56377 | 30.1 | 15.416282 |
| 701 | Harrison County | TX | 05000US48203 | -94.374425 | 32.547993 | 66534 | NaN | NaN | NaN | NaN | NaN | NaN | 1796.0 | 4719.0 | 14211.0 | 14596.0 | 7316.0 | 11727 | 37.2 | 0.4255 | 46548 | 29.7 | 30.738775 |
| 702 | Hays County | TX | 05000US48209 | -98.029267 | 30.061225 | 204470 | 184248.0 | 8627.0 | 3314.0 | 315.0 | 0.0 | 3753.0 | 6280.0 | 7686.0 | 26041.0 | 36282.0 | 43736.0 | 27807 | 31.2 | 0.4471 | 64658 | 31.1 | 9.008377 |
| 703 | Henderson County | TX | 05000US48213 | -95.853418 | 32.211633 | 79901 | NaN | NaN | NaN | NaN | NaN | NaN | 2042.0 | 5264.0 | 18740.0 | 17519.0 | 10334.0 | 11545 | 43.7 | 0.4776 | 44088 | 24.4 | 22.803671 |
| 704 | Hidalgo County | TX | 05000US48215 | -98.180990 | 26.396384 | 849843 | 717201.0 | 2922.0 | 9403.0 | 1953.0 | 93.0 | 105943.0 | 78268.0 | 61965.0 | 110109.0 | 113444.0 | 86415.0 | 264099 | 29.2 | 0.4952 | 36176 | 32.3 | 29.879940 |
| 705 | Hunt County | TX | 05000US48231 | -96.083807 | 33.123438 | 92073 | 71701.0 | 7680.0 | 1488.0 | 1185.0 | 0.0 | 9273.0 | 1875.0 | 7375.0 | 17973.0 | 19094.0 | 12161.0 | 15098 | 38.1 | 0.4414 | 53962 | 29.1 | 16.174268 |
| 706 | Jefferson County | TX | 05000US48245 | -94.149331 | 29.854000 | 254679 | 150261.0 | 86060.0 | 10050.0 | 816.0 | 198.0 | 4262.0 | 9005.0 | 15139.0 | 56965.0 | 52466.0 | 31276.0 | 48241 | 35.9 | 0.4885 | 45390 | 31.1 | 32.916149 |
| 707 | Johnson County | TX | 05000US48251 | -97.364823 | 32.379511 | 163274 | 149251.0 | 5440.0 | 1305.0 | 1179.0 | 751.0 | 1997.0 | 3828.0 | 9733.0 | 40079.0 | 30365.0 | 20842.0 | 17098 | 36.9 | 0.4009 | 59895 | 26.7 | 11.861679 |
| 708 | Kaufman County | TX | 05000US48257 | -96.288378 | 32.598944 | 118350 | 98799.0 | 12733.0 | 1429.0 | 324.0 | 0.0 | 2852.0 | 4232.0 | 5371.0 | 27916.0 | 22837.0 | 14754.0 | 14678 | 36.0 | 0.4006 | 62033 | 29.0 | 11.750600 |
| 709 | Liberty County | TX | 05000US48291 | -94.822681 | 30.162188 | 81704 | NaN | NaN | NaN | NaN | NaN | NaN | 3349.0 | 7752.0 | 21829.0 | 14329.0 | 4890.0 | 10822 | 36.7 | 0.4630 | 42877 | 27.0 | 23.862769 |
| 710 | Lubbock County | TX | 05000US48303 | -101.819944 | 33.611469 | 303137 | 248032.0 | 21502.0 | 5857.0 | 3901.0 | 1000.0 | 15506.0 | 7934.0 | 14974.0 | 44018.0 | 53559.0 | 55222.0 | 56791 | 30.6 | 0.4732 | 49136 | 29.3 | 15.691896 |
| 711 | McLennan County | TX | 05000US48309 | -97.201472 | 31.549493 | 247934 | 191192.0 | 36482.0 | 3858.0 | 1100.0 | 149.0 | 9396.0 | 7785.0 | 15462.0 | 43577.0 | 47971.0 | 32620.0 | 44677 | 33.3 | 0.4899 | 46860 | 29.9 | 19.466976 |
| 712 | Midland County | TX | 05000US48329 | -102.024326 | 31.870896 | 162565 | NaN | NaN | NaN | NaN | NaN | NaN | 4869.0 | 9202.0 | 27689.0 | 30158.0 | 26830.0 | 13225 | 32.0 | 0.4781 | 65349 | 26.7 | 14.578661 |
| 713 | Montgomery County | TX | 05000US48339 | -95.503523 | 30.302364 | 556203 | 480382.0 | 30394.0 | 15327.0 | 3642.0 | 358.0 | 12616.0 | 12690.0 | 30061.0 | 85473.0 | 108245.0 | 120923.0 | 63175 | 37.2 | 0.4917 | 71123 | 26.5 | 9.595274 |
| 714 | Nacogdoches County | TX | 05000US48347 | -94.620250 | 31.620560 | 65806 | NaN | NaN | NaN | NaN | NaN | NaN | 1540.0 | 5393.0 | 9178.0 | 10581.0 | 9527.0 | 16910 | 30.7 | 0.4954 | 35562 | 36.7 | 21.538128 |
| 715 | Nueces County | TX | 05000US48355 | -97.521643 | 27.739406 | 361350 | 326313.0 | 14774.0 | 6628.0 | 1487.0 | 211.0 | 5624.0 | 12329.0 | 23482.0 | 65369.0 | 77123.0 | 51583.0 | 50456 | 35.0 | 0.4400 | 54318 | 28.1 | 17.160011 |
| 716 | Orange County | TX | 05000US48361 | -93.893358 | 30.120918 | 84964 | NaN | NaN | NaN | NaN | NaN | NaN | 1265.0 | 4433.0 | 22395.0 | 18921.0 | 8783.0 | 10662 | 37.4 | 0.4460 | 53480 | 23.5 | 17.567152 |
| 717 | Parker County | TX | 05000US48367 | -97.805905 | 32.777096 | 129441 | 121518.0 | 1965.0 | 731.0 | 984.0 | 72.0 | 2346.0 | 2716.0 | 6368.0 | 27335.0 | 30508.0 | 19066.0 | 11619 | 40.0 | 0.4220 | 66548 | 25.6 | 12.061772 |
| 718 | Potter County | TX | 05000US48375 | -101.893804 | 35.398675 | 120832 | 94819.0 | 12161.0 | 5672.0 | 302.0 | 202.0 | 2918.0 | 7043.0 | 10083.0 | 24537.0 | 22006.0 | 11283.0 | 25458 | 33.8 | 0.4804 | 42305 | 27.7 | 23.542521 |
| 719 | Randall County | TX | 05000US48381 | -101.895547 | 34.962529 | 132501 | 118130.0 | 3530.0 | 2313.0 | 998.0 | 188.0 | 4183.0 | 1519.0 | 6223.0 | 16860.0 | 34079.0 | 27418.0 | 11370 | 35.5 | 0.4492 | 67015 | 26.6 | 8.275016 |
| 720 | Rockwall County | TX | 05000US48397 | -96.407501 | 32.889216 | 93978 | NaN | NaN | NaN | NaN | NaN | NaN | 1366.0 | 2431.0 | 11993.0 | 18979.0 | 24151.0 | 3646 | 37.5 | 0.3691 | 95731 | 26.8 | 9.108159 |
| 721 | San Patricio County | TX | 05000US48409 | -97.517165 | 28.011782 | 67655 | NaN | NaN | NaN | NaN | NaN | NaN | 2374.0 | 5015.0 | 12709.0 | 14982.0 | 6261.0 | 9592 | 35.6 | 0.4279 | 53348 | 29.4 | 26.417238 |
| 722 | Smith County | TX | 05000US48423 | -95.269630 | 32.377092 | 225290 | 170784.0 | 39062.0 | 3832.0 | 1092.0 | 159.0 | 6557.0 | 8219.0 | 11593.0 | 35013.0 | 54675.0 | 36674.0 | 35747 | 36.5 | 0.4416 | 52572 | 31.7 | 19.711843 |
| 723 | Tarrant County | TX | 05000US48439 | -97.291291 | 32.772040 | 2016872 | 1367728.0 | 320375.0 | 109096.0 | 9482.0 | 2648.0 | 141326.0 | 69887.0 | 99460.0 | 317859.0 | 385894.0 | 395920.0 | 272364 | 34.3 | 0.4600 | 61534 | 28.8 | 11.320343 |
| 724 | Taylor County | TX | 05000US48441 | -99.893220 | 32.295684 | 136535 | 105853.0 | 10686.0 | 3333.0 | 1134.0 | 8.0 | 11670.0 | 2703.0 | 5237.0 | 30230.0 | 25366.0 | 18949.0 | 23270 | 32.8 | 0.4481 | 48803 | 29.9 | 23.455469 |
| 725 | Tom Green County | TX | 05000US48451 | -100.461355 | 31.401583 | 118386 | 105757.0 | 5937.0 | 1289.0 | 506.0 | 0.0 | 2978.0 | 2804.0 | 8372.0 | 21390.0 | 23587.0 | 18973.0 | 13387 | 33.8 | 0.4490 | 48696 | 29.7 | 22.454422 |
| 726 | Travis County | TX | 05000US48453 | -97.691270 | 30.239513 | 1199323 | 892987.0 | 99449.0 | 76749.0 | 5241.0 | 1443.0 | 81863.0 | 40242.0 | 40876.0 | 136023.0 | 203573.0 | 385317.0 | 144605 | 33.7 | 0.4810 | 70158 | 29.2 | 8.919202 |
| 727 | Victoria County | TX | 05000US48469 | -96.971198 | 28.796370 | 92467 | NaN | NaN | NaN | NaN | NaN | NaN | 2730.0 | 5441.0 | 18853.0 | 20524.0 | 11553.0 | 12549 | 36.1 | 0.5174 | 53778 | 27.8 | 25.219387 |
| 728 | Walker County | TX | 05000US48471 | -95.569888 | 30.743090 | 71484 | NaN | NaN | NaN | NaN | NaN | NaN | 1681.0 | 2980.0 | 18335.0 | 11933.0 | 9721.0 | 11540 | 34.1 | 0.4595 | 42662 | 33.8 | 11.768589 |
| 729 | Webb County | TX | 05000US48479 | -99.326641 | 27.770584 | 271193 | NaN | NaN | NaN | NaN | NaN | NaN | 20643.0 | 21876.0 | 42525.0 | 31289.0 | 25535.0 | 88321 | 28.1 | 0.5040 | 35659 | 37.9 | 36.741831 |
| 730 | Wichita County | TX | 05000US48485 | -98.716851 | 33.991103 | 131838 | 104044.0 | 12709.0 | 3735.0 | 1428.0 | 151.0 | 3835.0 | 2238.0 | 7364.0 | 24748.0 | 27356.0 | 20050.0 | 19269 | 34.0 | 0.4503 | 44769 | 29.0 | 19.724771 |
| 731 | Williamson County | TX | 05000US48491 | -97.605069 | 30.649030 | 528718 | 421797.0 | 31597.0 | 35179.0 | 1678.0 | 143.0 | 15041.0 | 7009.0 | 11878.0 | 70903.0 | 114289.0 | 141071.0 | 29441 | 36.2 | 0.3942 | 81818 | 27.8 | 5.321011 |
| 732 | Cache County | UT | 05000US49005 | -111.744580 | 41.734225 | 122753 | 110095.0 | 968.0 | 3234.0 | 373.0 | 473.0 | 5253.0 | 2239.0 | 2911.0 | 11587.0 | 21136.0 | 23924.0 | 15452 | 25.3 | 0.4386 | 58003 | 29.5 | 7.744071 |
| 733 | Davis County | UT | 05000US49011 | -112.202123 | 41.037045 | 342281 | 304284.0 | 4795.0 | 6019.0 | 1841.0 | 2845.0 | 11341.0 | 1564.0 | 6085.0 | 42164.0 | 73386.0 | 74648.0 | 19800 | 30.9 | 0.3829 | 76905 | 25.2 | 8.553514 |
| 734 | Salt Lake County | UT | 05000US49035 | -111.924244 | 40.667882 | 1121354 | 882231.0 | 19235.0 | 44706.0 | 6570.0 | 16997.0 | 114439.0 | 23448.0 | 41677.0 | 159835.0 | 234564.0 | 236745.0 | 100511 | 32.7 | 0.4351 | 68665 | 27.9 | 8.746201 |
| 735 | Utah County | UT | 05000US49049 | -111.668667 | 40.120409 | 592299 | 548560.0 | 3287.0 | 8277.0 | 4182.0 | 5381.0 | 5515.0 | 3935.0 | 10613.0 | 47170.0 | 115230.0 | 112447.0 | 66456 | 24.6 | 0.4150 | 69799 | 28.2 | 4.965685 |
| 736 | Washington County | UT | 05000US49053 | -113.487800 | 37.262531 | 160245 | 143282.0 | 624.0 | 1461.0 | 2161.0 | 1646.0 | 7247.0 | 1452.0 | 3421.0 | 22390.0 | 44565.0 | 28692.0 | 21104 | 35.9 | 0.4430 | 55056 | 32.4 | 11.196740 |
| 737 | Weber County | UT | 05000US49057 | -111.875879 | 41.270355 | 247560 | 223579.0 | 2438.0 | 2914.0 | 1843.0 | 281.0 | 9278.0 | 3374.0 | 10281.0 | 45014.0 | 58359.0 | 33891.0 | 26247 | 32.4 | 0.4076 | 63158 | 25.4 | 11.979724 |
| 738 | Albemarle County | VA | 05000US51003 | -78.553506 | 38.024184 | 106878 | 86536.0 | 10426.0 | 5383.0 | 255.0 | 70.0 | 1618.0 | 1231.0 | 4042.0 | 11103.0 | 15694.0 | 37889.0 | 10006 | 38.2 | 0.4550 | 71975 | 28.1 | 11.466343 |
| 739 | Arlington County | VA | 05000US51013 | -77.100703 | 38.878337 | 230050 | 162749.0 | 19802.0 | 23558.0 | 962.0 | 411.0 | 13518.0 | 5284.0 | 3702.0 | 15646.0 | 17891.0 | 126910.0 | 18298 | 34.8 | 0.4399 | 110388 | 27.1 | 6.814909 |
| 740 | Augusta County | VA | 05000US51015 | -79.146682 | 38.167807 | 74997 | NaN | NaN | NaN | NaN | NaN | NaN | 1179.0 | 6000.0 | 18869.0 | 14398.0 | 12730.0 | 6714 | 45.2 | 0.4082 | 55342 | 23.3 | 23.466154 |
| 741 | Bedford County | VA | 05000US51019 | -79.527947 | 37.312408 | 77960 | NaN | NaN | NaN | NaN | NaN | NaN | 583.0 | 3634.0 | 17232.0 | 17212.0 | 16633.0 | 7653 | 45.8 | 0.4394 | 56479 | 24.4 | 22.968951 |
| 742 | Chesterfield County | VA | 05000US51041 | -77.585847 | 37.378434 | 339009 | 229400.0 | 76274.0 | 13052.0 | 672.0 | 254.0 | 8584.0 | 4418.0 | 9175.0 | 54002.0 | 65748.0 | 92193.0 | 23030 | 39.2 | 0.4042 | 76059 | 28.5 | 8.425721 |
| 743 | Fairfax County | VA | 05000US51059 | -77.276117 | 38.833742 | 1138652 | 705214.0 | 110990.0 | 217179.0 | 2448.0 | 979.0 | 53939.0 | 29448.0 | 26864.0 | 93196.0 | 144594.0 | 472131.0 | 66681 | 38.1 | 0.4260 | 115717 | 28.3 | 4.397668 |
| 744 | Fauquier County | VA | 05000US51061 | -77.821585 | 38.744103 | 69069 | NaN | NaN | NaN | NaN | NaN | NaN | 511.0 | 2979.0 | 11744.0 | 14820.0 | 16483.0 | 3069 | 41.5 | 0.4285 | 94347 | 26.0 | 12.919240 |
| 745 | Frederick County | VA | 05000US51069 | -78.263916 | 39.203637 | 84421 | NaN | NaN | NaN | NaN | NaN | NaN | 1026.0 | 3574.0 | 19234.0 | 16832.0 | 16515.0 | 3717 | 40.9 | 0.3798 | 69827 | 30.6 | 13.237971 |
| 746 | Hanover County | VA | 05000US51085 | -77.490992 | 37.760165 | 104392 | 90528.0 | 8977.0 | 1023.0 | 281.0 | 156.0 | 235.0 | 731.0 | 4351.0 | 17270.0 | 19484.0 | 29212.0 | 6064 | 44.1 | 0.4050 | 83135 | 27.0 | 11.560465 |
| 747 | Henrico County | VA | 05000US51087 | -77.300333 | 37.437521 | 326501 | 188411.0 | 95882.0 | 27058.0 | 591.0 | 283.0 | 3972.0 | 3064.0 | 12625.0 | 49896.0 | 62947.0 | 94750.0 | 29593 | 38.8 | 0.4716 | 66337 | 28.8 | 13.298679 |
| 748 | James City County | VA | 05000US51095 | -76.778319 | 37.324427 | 74404 | NaN | NaN | NaN | NaN | NaN | NaN | 391.0 | 1882.0 | 9119.0 | 15025.0 | 27021.0 | 3758 | 47.2 | 0.4628 | 83455 | 32.1 | 8.606039 |
| 749 | Loudoun County | VA | 05000US51107 | -77.638857 | 39.081130 | 385945 | 258094.0 | 27802.0 | 67889.0 | 1213.0 | 769.0 | 11962.0 | 8900.0 | 7622.0 | 31121.0 | 51199.0 | 147019.0 | 13850 | 35.9 | 0.3700 | 134464 | 26.7 | 3.812962 |
| 750 | Montgomery County | VA | 05000US51121 | -80.387314 | 37.174884 | 98602 | 84345.0 | 4420.0 | 5442.0 | 419.0 | 30.0 | 974.0 | 642.0 | 2093.0 | 12037.0 | 12015.0 | 27611.0 | 19715 | 28.2 | 0.5297 | 55706 | 31.9 | 7.567538 |
| 751 | Prince William County | VA | 05000US51153 | -77.478887 | 38.702332 | 455210 | 254644.0 | 93401.0 | 40673.0 | 1594.0 | 356.0 | 41139.0 | 15463.0 | 16532.0 | 61094.0 | 75872.0 | 117211.0 | 36462 | 34.9 | 0.3888 | 97986 | 31.1 | 5.030331 |
| 752 | Roanoke County | VA | 05000US51161 | -80.190110 | 37.331077 | 94031 | 82416.0 | 5908.0 | 2895.0 | 232.0 | 0.0 | 1166.0 | 1021.0 | 3668.0 | 16022.0 | 23310.0 | 22451.0 | 7242 | 43.5 | 0.4540 | 60454 | 24.7 | 15.819517 |
| 753 | Rockingham County | VA | 05000US51165 | -78.876307 | 38.511257 | 79744 | NaN | NaN | NaN | NaN | NaN | NaN | 1173.0 | 5149.0 | 18862.0 | 12198.0 | 14586.0 | 7805 | 41.8 | 0.4467 | 57755 | 26.4 | 19.191304 |
| 754 | Spotsylvania County | VA | 05000US51177 | -77.656280 | 38.182311 | 132010 | 94731.0 | 21641.0 | 3101.0 | 164.0 | 49.0 | 7674.0 | 2420.0 | 5072.0 | 26384.0 | 25825.0 | 26472.0 | 9409 | 39.0 | 0.3565 | 81146 | 31.5 | 8.189001 |
| 755 | Stafford County | VA | 05000US51179 | -77.459043 | 38.418933 | 144361 | 96123.0 | 25628.0 | 5862.0 | 666.0 | 90.0 | 8659.0 | 1272.0 | 4754.0 | 20290.0 | 29233.0 | 36275.0 | 7905 | 35.9 | 0.4034 | 97484 | 31.3 | 4.583449 |
| 756 | York County | VA | 05000US51199 | -76.395533 | 37.220914 | 67976 | 50925.0 | 8714.0 | 4245.0 | 241.0 | 612.0 | 566.0 | 874.0 | 1203.0 | 9490.0 | 12358.0 | 20903.0 | 2623 | 39.5 | 0.3822 | 89418 | 28.0 | 6.051183 |
| 757 | Alexandria city | VA | 05000US51510 | -77.082026 | 38.818343 | 155810 | 92717.0 | 34500.0 | 9726.0 | 99.0 | 146.0 | 11250.0 | 5011.0 | 4248.0 | 12739.0 | 21383.0 | 73206.0 | 19256 | 36.6 | 0.4621 | 87920 | 28.9 | 6.691950 |
| 758 | Chesapeake city | VA | 05000US51550 | -76.301788 | 36.679376 | 237940 | 147623.0 | 71159.0 | 7197.0 | 68.0 | 0.0 | 2808.0 | 2991.0 | 10013.0 | 41274.0 | 54376.0 | 50669.0 | 18606 | 36.9 | 0.4120 | 72928 | 30.9 | 9.013483 |
| 759 | Hampton city | VA | 05000US51650 | -76.297149 | 37.048030 | 135410 | 56219.0 | 68730.0 | 3506.0 | 75.0 | 476.0 | 1902.0 | 1348.0 | 5919.0 | 26995.0 | 31164.0 | 24831.0 | 22696 | 36.2 | 0.4407 | 50435 | 30.0 | 12.426866 |
| 760 | Lynchburg city | VA | 05000US51680 | -79.195458 | 37.399016 | 80212 | 53004.0 | 23624.0 | 795.0 | 312.0 | 0.0 | 235.0 | 868.0 | 3665.0 | 11592.0 | 12204.0 | 14714.0 | 10175 | 27.7 | 0.4848 | 41264 | 28.6 | 20.333999 |
| 761 | Newport News city | VA | 05000US51700 | -76.521719 | 37.075978 | 181825 | 86739.0 | 73750.0 | 4472.0 | 430.0 | 202.0 | 3401.0 | 1231.0 | 8391.0 | 31020.0 | 42624.0 | 31728.0 | 23008 | 33.4 | 0.4361 | 50524 | 33.1 | 13.016798 |
| 762 | Norfolk city | VA | 05000US51710 | -76.244641 | 36.923015 | 245115 | 115675.0 | 100685.0 | 9313.0 | 832.0 | 496.0 | 8111.0 | 2760.0 | 12610.0 | 38949.0 | 52402.0 | 42731.0 | 43790 | 30.6 | 0.4855 | 46467 | 32.5 | 12.875668 |
| 763 | Portsmouth city | VA | 05000US51740 | -76.356269 | 36.859430 | 95252 | 38231.0 | 49467.0 | 1626.0 | 265.0 | 1001.0 | 1566.0 | 1390.0 | 5615.0 | 18851.0 | 22274.0 | 13217.0 | 15487 | 35.0 | 0.4236 | 48516 | 36.5 | 17.025809 |
| 764 | Richmond city | VA | 05000US51760 | -77.476008 | 37.531399 | 223170 | 101987.0 | 105155.0 | 4903.0 | 650.0 | 115.0 | 1948.0 | 4139.0 | 14529.0 | 33617.0 | 39174.0 | 59674.0 | 56983 | 33.4 | 0.5550 | 42373 | 35.1 | 22.989183 |
| 765 | Roanoke city | VA | 05000US51770 | -79.958472 | 37.277830 | 99660 | NaN | NaN | NaN | NaN | NaN | NaN | 1254.0 | 6779.0 | 24672.0 | 21515.0 | 14711.0 | 23965 | 38.7 | 0.4621 | 37044 | 32.4 | 21.706583 |
| 766 | Suffolk city | VA | 05000US51800 | -76.634781 | 36.697157 | 89273 | 45912.0 | 37324.0 | 1688.0 | 191.0 | 89.0 | 583.0 | 979.0 | 4270.0 | 17173.0 | 20929.0 | 15757.0 | 10024 | 38.2 | 0.4355 | 66669 | 32.8 | 15.432783 |
| 767 | Virginia Beach city | VA | 05000US51810 | -76.024020 | 36.779322 | 452602 | 303452.0 | 82660.0 | 30196.0 | 901.0 | 175.0 | 9160.0 | 3413.0 | 14265.0 | 65040.0 | 114133.0 | 107278.0 | 34792 | 35.8 | 0.4180 | 71117 | 29.9 | 8.198214 |
| 768 | Chittenden County | VT | 05000US50007 | -73.070525 | 44.460676 | 161531 | 145945.0 | 4444.0 | 5969.0 | 481.0 | 44.0 | 1023.0 | 1352.0 | 2924.0 | 21528.0 | 25888.0 | 53549.0 | 15818 | 37.2 | 0.4477 | 68843 | 31.9 | 8.594509 |
| 769 | Benton County | WA | 05000US53005 | -119.516864 | 46.228072 | 193686 | 161191.0 | 3631.0 | 4742.0 | 1070.0 | 81.0 | 16728.0 | 5501.0 | 4794.0 | 29319.0 | 46846.0 | 37187.0 | 18867 | 35.6 | 0.4243 | 62508 | 27.2 | 8.713122 |
| 770 | Chelan County | WA | 05000US53007 | -120.618543 | 47.859891 | 76338 | NaN | NaN | NaN | NaN | NaN | NaN | 3165.0 | 5245.0 | 13673.0 | 14361.0 | 13546.0 | 6174 | 40.3 | 0.4566 | 52080 | 24.5 | 20.900787 |
| 771 | Clallam County | WA | 05000US53009 | -123.930611 | 48.113009 | 74570 | 65652.0 | 883.0 | 774.0 | 3385.0 | 48.0 | 728.0 | 365.0 | 4650.0 | 16323.0 | 20768.0 | 13464.0 | 11565 | 50.8 | 0.4144 | 48587 | 28.6 | 14.860068 |
| 772 | Clark County | WA | 05000US53011 | -122.485903 | 45.771674 | 467018 | 394477.0 | 8011.0 | 22913.0 | 2095.0 | 3821.0 | 14914.0 | 5029.0 | 16936.0 | 75961.0 | 121443.0 | 92706.0 | 40514 | 38.0 | 0.4269 | 69062 | 28.9 | 7.686018 |
| 773 | Cowlitz County | WA | 05000US53015 | -122.658682 | 46.185923 | 105160 | 95117.0 | 403.0 | 926.0 | 1316.0 | 69.0 | 1800.0 | 1215.0 | 6460.0 | 22978.0 | 28857.0 | 12522.0 | 17297 | 42.4 | 0.4521 | 50637 | 31.6 | 14.702514 |
| 774 | Franklin County | WA | 05000US53021 | -118.906944 | 46.534580 | 90160 | 54718.0 | 1733.0 | 1777.0 | 726.0 | 759.0 | 26894.0 | 6511.0 | 4749.0 | 12158.0 | 17898.0 | 8272.0 | 14024 | 29.3 | 0.4107 | 57670 | 29.3 | 13.044821 |
| 775 | Grant County | WA | 05000US53025 | -119.467788 | 47.213633 | 93546 | 59251.0 | 558.0 | 773.0 | 843.0 | 0.0 | 26679.0 | 4197.0 | 4928.0 | 14719.0 | 18172.0 | 10194.0 | 15671 | 32.9 | 0.4275 | 48335 | 23.8 | 16.094670 |
| 776 | Grays Harbor County | WA | 05000US53027 | -123.827043 | 47.142786 | 71628 | 62373.0 | 1187.0 | 1304.0 | 2996.0 | 132.0 | 514.0 | 1809.0 | 4237.0 | 18248.0 | 18355.0 | 8684.0 | 8630 | 43.9 | 0.4564 | 49623 | 29.6 | 11.961215 |
| 777 | Island County | WA | 05000US53029 | -122.670503 | 48.158436 | 82636 | 70785.0 | 1993.0 | 3901.0 | 898.0 | 318.0 | 914.0 | 562.0 | 1956.0 | 14168.0 | 24536.0 | 18813.0 | 8590 | 45.4 | 0.4359 | 64813 | 27.6 | 6.638693 |
| 778 | King County | WA | 05000US53033 | -121.832375 | 47.493554 | 2149970 | 1393480.0 | 130762.0 | 361162.0 | 11288.0 | 17123.0 | 97649.0 | 42414.0 | 62934.0 | 230070.0 | 393756.0 | 784298.0 | 196355 | 37.1 | 0.4693 | 86095 | 27.9 | 6.319427 |
| 779 | Kitsap County | WA | 05000US53035 | -122.649636 | 47.639687 | 264811 | 213321.0 | 6587.0 | 11318.0 | 3022.0 | 2204.0 | 6781.0 | 2618.0 | 7864.0 | 41891.0 | 71354.0 | 58621.0 | 25833 | 38.5 | 0.4233 | 69171 | 26.7 | 8.445512 |
| 780 | Lewis County | WA | 05000US53041 | -122.377444 | 46.580071 | 77066 | 69504.0 | 542.0 | 932.0 | 1279.0 | 26.0 | 1836.0 | 1429.0 | 3770.0 | 16293.0 | 22850.0 | 9063.0 | 10097 | 43.7 | 0.4140 | 45523 | 34.5 | 16.120506 |
| 781 | Pierce County | WA | 05000US53053 | -122.144709 | 47.040716 | 861312 | 629794.0 | 55912.0 | 52850.0 | 10825.0 | 12192.0 | 28744.0 | 12416.0 | 34003.0 | 155404.0 | 213234.0 | 155159.0 | 102454 | 36.1 | 0.4356 | 64434 | 30.1 | 10.489154 |
| 782 | Skagit County | WA | 05000US53057 | -121.816278 | 48.493066 | 123681 | 102076.0 | 736.0 | 2563.0 | 2356.0 | 354.0 | 11035.0 | 2894.0 | 5001.0 | 22387.0 | 33643.0 | 20920.0 | 13339 | 42.0 | 0.4279 | 60983 | 28.4 | 8.911531 |
| 783 | Snohomish County | WA | 05000US53061 | -121.766412 | 48.054913 | 787620 | 598184.0 | 24290.0 | 80706.0 | 7930.0 | 4176.0 | 27135.0 | 9618.0 | 28077.0 | 132054.0 | 198041.0 | 170822.0 | 61249 | 38.0 | 0.4074 | 78716 | 30.0 | 7.302632 |
| 784 | Spokane County | WA | 05000US53063 | -117.404392 | 47.620379 | 499072 | 437088.0 | 9113.0 | 11616.0 | 8027.0 | 2794.0 | 9051.0 | 3350.0 | 14139.0 | 79505.0 | 135148.0 | 104699.0 | 63748 | 37.3 | 0.4535 | 53043 | 30.0 | 10.476132 |
| 785 | Thurston County | WA | 05000US53067 | -122.829441 | 46.932598 | 275222 | 226065.0 | 8506.0 | 16030.0 | 3433.0 | 2555.0 | 2870.0 | 2907.0 | 7961.0 | 40089.0 | 73967.0 | 68492.0 | 28522 | 39.5 | 0.4119 | 65783 | 33.3 | 9.578417 |
| 786 | Whatcom County | WA | 05000US53073 | -121.836433 | 48.842653 | 216800 | 178402.0 | 2619.0 | 9298.0 | 6037.0 | 524.0 | 11147.0 | 3479.0 | 7136.0 | 35712.0 | 47627.0 | 46819.0 | 33560 | 37.0 | 0.4351 | 56411 | 32.2 | 9.871326 |
| 787 | Yakima County | WA | 05000US53077 | -120.740145 | 46.456558 | 249636 | 194416.0 | 3499.0 | 2502.0 | 10785.0 | 35.0 | 30169.0 | 16206.0 | 16593.0 | 43273.0 | 45612.0 | 24032.0 | 44367 | 32.8 | 0.4486 | 48965 | 27.6 | 19.457600 |
| 788 | Brown County | WI | 05000US55009 | -87.995926 | 44.473961 | 260401 | 221450.0 | 5768.0 | 7972.0 | 7072.0 | 22.0 | 8699.0 | 3177.0 | 6941.0 | 55002.0 | 57656.0 | 49247.0 | 24823 | 37.1 | 0.4367 | 57783 | 24.5 | 14.538567 |
| 789 | Dane County | WI | 05000US55025 | -89.417852 | 43.067468 | 531273 | 443862.0 | 25227.0 | 30922.0 | 2137.0 | 243.0 | 11260.0 | 3904.0 | 7464.0 | 61626.0 | 92168.0 | 180349.0 | 58325 | 35.0 | 0.4402 | 70796 | 28.6 | 7.865530 |
| 790 | Dodge County | WI | 05000US55027 | -88.704379 | 43.422706 | 88068 | 85269.0 | 1584.0 | 107.0 | 105.0 | 0.0 | 149.0 | 922.0 | 4325.0 | 25133.0 | 21761.0 | 10395.0 | 8242 | 42.1 | 0.3951 | 55856 | 25.1 | 17.533480 |
| 791 | Eau Claire County | WI | 05000US55035 | -91.286414 | 44.726355 | 102965 | NaN | NaN | NaN | NaN | NaN | NaN | 996.0 | 2701.0 | 16107.0 | 23900.0 | 19697.0 | 13273 | 34.2 | 0.4524 | 49821 | 30.0 | 13.293958 |
| 792 | Fond du Lac County | WI | 05000US55039 | -88.493284 | 43.754722 | 102144 | 94027.0 | 762.0 | 1494.0 | 528.0 | 25.0 | 3086.0 | 1363.0 | 4110.0 | 26412.0 | 21726.0 | 16894.0 | 6554 | 41.0 | 0.3974 | 58310 | 27.7 | 15.731961 |
| 793 | Jefferson County | WI | 05000US55055 | -88.773985 | 43.013807 | 84625 | NaN | NaN | NaN | NaN | NaN | NaN | 1583.0 | 3612.0 | 18712.0 | 19172.0 | 14083.0 | 8002 | 39.6 | 0.3854 | 58703 | 28.6 | 16.041757 |
| 794 | Kenosha County | WI | 05000US55059 | -87.424898 | 42.579703 | 168183 | 143113.0 | 12119.0 | 2482.0 | 827.0 | 175.0 | 3251.0 | 2685.0 | 9477.0 | 33687.0 | 36205.0 | 27922.0 | 22835 | 39.0 | 0.4409 | 59417 | 28.7 | 15.200509 |
| 795 | La Crosse County | WI | 05000US55063 | -91.111758 | 43.908222 | 118122 | 107056.0 | 1327.0 | 5207.0 | 217.0 | 9.0 | 1091.0 | 379.0 | 2785.0 | 16437.0 | 27267.0 | 27709.0 | 15979 | 35.5 | 0.4375 | 54823 | 29.9 | 12.955018 |
| 796 | Manitowoc County | WI | 05000US55071 | -87.313828 | 44.105108 | 79536 | 74773.0 | 485.0 | 2022.0 | 365.0 | 0.0 | 217.0 | 1197.0 | 3164.0 | 24440.0 | 16900.0 | 10924.0 | 6928 | 44.6 | 0.3868 | 51752 | 22.7 | 18.309283 |
| 797 | Marathon County | WI | 05000US55073 | -89.757823 | 44.898036 | 135603 | 123417.0 | 483.0 | 7976.0 | 456.0 | 0.0 | 760.0 | 1748.0 | 5219.0 | 32944.0 | 29059.0 | 23013.0 | 14934 | 40.8 | 0.4412 | 54774 | 23.6 | 15.839484 |
| 798 | Milwaukee County | WI | 05000US55079 | -87.481575 | 43.017655 | 951448 | 562559.0 | 249436.0 | 40031.0 | 4836.0 | 168.0 | 57968.0 | 18884.0 | 48539.0 | 178628.0 | 181332.0 | 191805.0 | 181954 | 34.7 | 0.4870 | 47607 | 30.0 | 18.257727 |
| 799 | Outagamie County | WI | 05000US55087 | -88.464988 | 44.418226 | 184526 | 165748.0 | 2474.0 | 6610.0 | 2854.0 | 65.0 | 3225.0 | 2075.0 | 4831.0 | 39193.0 | 43459.0 | 33885.0 | 15680 | 38.2 | 0.4173 | 61149 | 24.5 | 12.748993 |
| 800 | Ozaukee County | WI | 05000US55089 | -87.496553 | 43.360715 | 88314 | NaN | NaN | NaN | NaN | NaN | NaN | 190.0 | 1689.0 | 13114.0 | 16375.0 | 29372.0 | 5667 | 44.0 | 0.4987 | 84415 | 27.1 | 10.667194 |
| 801 | Portage County | WI | 05000US55097 | -89.498070 | 44.476246 | 70447 | 66177.0 | 485.0 | 1476.0 | 162.0 | 0.0 | 508.0 | 313.0 | 1808.0 | 13982.0 | 14493.0 | 14245.0 | 7926 | 36.6 | 0.4258 | 53655 | 27.5 | 13.642044 |
| 802 | Racine County | WI | 05000US55101 | -87.414676 | 42.754075 | 195140 | 156136.0 | 21484.0 | 2903.0 | 1268.0 | 0.0 | 7096.0 | 2584.0 | 9481.0 | 46963.0 | 41936.0 | 30594.0 | 26423 | 40.0 | 0.4328 | 55706 | 28.6 | 13.693181 |
| 803 | Rock County | WI | 05000US55105 | -89.075119 | 42.669931 | 161620 | 140747.0 | 7575.0 | 1734.0 | 563.0 | 0.0 | 6456.0 | 2024.0 | 7586.0 | 37499.0 | 37683.0 | 23697.0 | 21082 | 40.1 | 0.4274 | 50729 | 28.8 | 17.563506 |
| 804 | St. Croix County | WI | 05000US55109 | -92.447284 | 45.028959 | 88029 | 84757.0 | 746.0 | 769.0 | 297.0 | 6.0 | 143.0 | 300.0 | 950.0 | 13731.0 | 22590.0 | 21288.0 | 5623 | 38.3 | 0.4264 | 72865 | 23.8 | 14.426181 |
| 805 | Sheboygan County | WI | 05000US55117 | -87.730545 | 43.746001 | 115427 | 104025.0 | 2495.0 | 6384.0 | 291.0 | 0.0 | 251.0 | 1129.0 | 3466.0 | 29073.0 | 25697.0 | 20649.0 | 6094 | 41.5 | 0.4102 | 54059 | 23.0 | 16.641484 |
| 806 | Walworth County | WI | 05000US55127 | -88.541731 | 42.668109 | 102959 | 96375.0 | 1042.0 | 1026.0 | 90.0 | 0.0 | 2260.0 | 881.0 | 3450.0 | 21587.0 | 21081.0 | 19720.0 | 11176 | 39.9 | 0.4511 | 58302 | 32.0 | 15.180199 |
| 807 | Washington County | WI | 05000US55131 | -88.232917 | 43.391156 | 134296 | NaN | NaN | NaN | NaN | NaN | NaN | 375.0 | 3703.0 | 27883.0 | 32721.0 | 28882.0 | 7163 | 42.9 | 0.4380 | 73502 | 25.8 | 12.062535 |
| 808 | Waukesha County | WI | 05000US55133 | -88.306707 | 43.019308 | 398424 | 367790.0 | 6221.0 | 13818.0 | 1459.0 | 156.0 | 3701.0 | 1559.0 | 7902.0 | 66421.0 | 82677.0 | 120152.0 | 20268 | 43.2 | 0.4535 | 81878 | 26.7 | 9.374261 |
| 809 | Winnebago County | WI | 05000US55139 | -88.668149 | 44.085707 | 169886 | 156755.0 | 3786.0 | 4567.0 | 1004.0 | 0.0 | 1112.0 | 1160.0 | 6549.0 | 38900.0 | 34015.0 | 32405.0 | 19587 | 38.6 | 0.4468 | 56754 | 26.8 | 12.467295 |
| 810 | Wood County | WI | 05000US55141 | -90.038825 | 44.461413 | 73107 | NaN | NaN | NaN | NaN | NaN | NaN | 718.0 | 2920.0 | 19447.0 | 17370.0 | 11131.0 | 6315 | 44.1 | 0.4425 | 51887 | 23.2 | 18.513542 |
| 811 | Berkeley County | WV | 05000US54003 | -78.032338 | 39.457854 | 113525 | NaN | NaN | NaN | NaN | NaN | NaN | 469.0 | 8584.0 | 29594.0 | 22489.0 | 16244.0 | 15881 | 38.2 | 0.4266 | 57357 | 28.1 | 11.213387 |
| 812 | Cabell County | WV | 05000US54011 | -82.243392 | 38.419580 | 95987 | 87445.0 | 4977.0 | 1307.0 | 15.0 | 0.0 | 149.0 | 1035.0 | 5584.0 | 19920.0 | 18593.0 | 16147.0 | 21347 | 38.0 | 0.5100 | 38823 | 31.5 | 20.924213 |
| 813 | Harrison County | WV | 05000US54033 | -80.386487 | 39.279199 | 68400 | NaN | NaN | NaN | NaN | NaN | NaN | 632.0 | 3851.0 | 18635.0 | 12263.0 | 12260.0 | 8853 | 41.8 | 0.4590 | 47204 | 27.6 | 16.470207 |
| 814 | Kanawha County | WV | 05000US54039 | -81.523522 | 38.328061 | 186241 | 164796.0 | 13019.0 | 2148.0 | 439.0 | 0.0 | 194.0 | 1898.0 | 12677.0 | 48425.0 | 35328.0 | 34075.0 | 31626 | 43.4 | 0.4983 | 45001 | 29.6 | 19.466407 |
| 815 | Monongalia County | WV | 05000US54061 | -80.059074 | 39.633645 | 104622 | NaN | NaN | NaN | NaN | NaN | NaN | 547.0 | 4002.0 | 19932.0 | 13517.0 | 26114.0 | 20350 | 31.4 | 0.5439 | 50953 | 32.0 | 12.628233 |
| 816 | Raleigh County | WV | 05000US54081 | -81.264671 | 37.762470 | 76601 | NaN | NaN | NaN | NaN | NaN | NaN | 599.0 | 4322.0 | 22513.0 | 15679.0 | 10792.0 | 10909 | 41.2 | 0.4284 | 45863 | 29.8 | 21.129769 |
| 817 | Wood County | WV | 05000US54107 | -81.515928 | 39.211679 | 85643 | NaN | NaN | NaN | NaN | NaN | NaN | 292.0 | 3520.0 | 19741.0 | 20671.0 | 15349.0 | 15465 | 43.0 | 0.4738 | 48655 | 30.1 | 16.747164 |
| 818 | Laramie County | WY | 05000US56021 | -104.660395 | 41.292830 | 98136 | 88958.0 | 2690.0 | 1200.0 | 443.0 | 362.0 | 1209.0 | 621.0 | 2900.0 | 17034.0 | 27792.0 | 18349.0 | 9270 | 36.4 | 0.3927 | 62221 | 28.7 | 11.497865 |
| 819 | Natrona County | WY | 05000US56025 | -106.764877 | 42.973641 | 81039 | NaN | NaN | NaN | NaN | NaN | NaN | 209.0 | 2832.0 | 18305.0 | 19822.0 | 12691.0 | 7256 | 36.0 | 0.4335 | 59474 | 29.0 | 12.860663 |
#df_transform = pd.DataFrame(pd.to_numeric(df.percent_no_internet, df.GEOID, df.P_total, df.P_white, df.P_black, df.P_asian, df.P_native, df.P_hawaiian, df.P_others, df.P_below_middle_school, df. P_some_high_school, df.P_some_high_school))
df_transform = pd.read_csv('~/Desktop/Python Exercises/kaggle_internet.csv')
pd.set_option('display.max_rows', 820)
pd.set_option('display.max_columns', 23)
df_transform.head(5)
| county | state | GEOID | lon | lat | P_total | P_white | P_black | P_asian | P_native | P_hawaiian | P_others | P_below_middle_school | P_some_high_school | P_high_school_equivalent | P_some_college | P_bachelor_and_above | P_below_poverty | median_age | gini_index | median_household_income | median_rent_per_income | percent_no_internet | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Anchorage Municipality | AK | 05000US02020 | -149.274354 | 61.177549 | 298192 | 184841.0 | 16102.0 | 27142.0 | 23916.0 | 7669.0 | 7935.0 | 2234.0 | 8196.0 | 44804.0 | 66162.0 | 70713.0 | 18302 | 33.0 | 0.4018 | 85634 | 28.0 | 6.593887 |
| 1 | Fairbanks North Star Borough | AK | 05000US02090 | -146.599867 | 64.690832 | 100605 | 75501.0 | 4385.0 | 3875.0 | 7427.0 | 503.0 | 2357.0 | 924.0 | 1527.0 | 14725.0 | 24570.0 | 19257.0 | 9580 | 30.6 | 0.3756 | 77328 | 25.6 | 12.102458 |
| 2 | Matanuska-Susitna Borough | AK | 05000US02170 | -149.407974 | 62.182173 | 104365 | 86314.0 | 1019.0 | 1083.0 | 5455.0 | 141.0 | 325.0 | 337.0 | 2755.0 | 21071.0 | 28472.0 | 12841.0 | 9893 | 34.2 | 0.4351 | 69332 | 29.6 | 11.156575 |
| 3 | Baldwin County | AL | 05000US01003 | -87.746067 | 30.659218 | 208563 | 180484.0 | 18821.0 | 914.0 | 1383.0 | 0.0 | 1469.0 | 3245.0 | 10506.0 | 41822.0 | 46790.0 | 43547.0 | 23375 | 42.4 | 0.4498 | 56732 | 29.3 | 17.868167 |
| 4 | Calhoun County | AL | 05000US01015 | -85.822513 | 33.771706 | 114611 | NaN | NaN | NaN | NaN | NaN | NaN | 2455.0 | 8853.0 | 24761.0 | 26625.0 | 12909.0 | 18193 | 39.1 | 0.4692 | 41687 | 24.8 | 23.464932 |
print(df_transform.dtypes)
county object state object GEOID object lon float64 lat float64 P_total int64 P_white float64 P_black float64 P_asian float64 P_native float64 P_hawaiian float64 P_others float64 P_below_middle_school float64 P_some_high_school float64 P_high_school_equivalent float64 P_some_college float64 P_bachelor_and_above float64 P_below_poverty int64 median_age float64 gini_index float64 median_household_income int64 median_rent_per_income float64 percent_no_internet float64 dtype: object
cols = ['P_total', 'P_white', 'P_black', 'P_asian', 'P_native', 'P_hawaiian', 'P_others', 'P_below_middle_school', 'P_some_high_school', 'P_high_school_equivalent', 'P_some_college', 'P_bachelor_and_above', 'P_below_poverty', 'median_age', 'median_household_income', 'median_rent_per_income','percent_no_internet']
df_transform[cols] = df_transform[cols].apply(pd.to_numeric, errors='coerce', axis=1)
df_transform.head(5)
| county | state | GEOID | lon | lat | P_total | P_white | P_black | P_asian | P_native | P_hawaiian | P_others | P_below_middle_school | P_some_high_school | P_high_school_equivalent | P_some_college | P_bachelor_and_above | P_below_poverty | median_age | gini_index | median_household_income | median_rent_per_income | percent_no_internet | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Anchorage Municipality | AK | 05000US02020 | -149.274354 | 61.177549 | 298192.0 | 184841.0 | 16102.0 | 27142.0 | 23916.0 | 7669.0 | 7935.0 | 2234.0 | 8196.0 | 44804.0 | 66162.0 | 70713.0 | 18302.0 | 33.0 | 0.4018 | 85634.0 | 28.0 | 6.593887 |
| 1 | Fairbanks North Star Borough | AK | 05000US02090 | -146.599867 | 64.690832 | 100605.0 | 75501.0 | 4385.0 | 3875.0 | 7427.0 | 503.0 | 2357.0 | 924.0 | 1527.0 | 14725.0 | 24570.0 | 19257.0 | 9580.0 | 30.6 | 0.3756 | 77328.0 | 25.6 | 12.102458 |
| 2 | Matanuska-Susitna Borough | AK | 05000US02170 | -149.407974 | 62.182173 | 104365.0 | 86314.0 | 1019.0 | 1083.0 | 5455.0 | 141.0 | 325.0 | 337.0 | 2755.0 | 21071.0 | 28472.0 | 12841.0 | 9893.0 | 34.2 | 0.4351 | 69332.0 | 29.6 | 11.156575 |
| 3 | Baldwin County | AL | 05000US01003 | -87.746067 | 30.659218 | 208563.0 | 180484.0 | 18821.0 | 914.0 | 1383.0 | 0.0 | 1469.0 | 3245.0 | 10506.0 | 41822.0 | 46790.0 | 43547.0 | 23375.0 | 42.4 | 0.4498 | 56732.0 | 29.3 | 17.868167 |
| 4 | Calhoun County | AL | 05000US01015 | -85.822513 | 33.771706 | 114611.0 | NaN | NaN | NaN | NaN | NaN | NaN | 2455.0 | 8853.0 | 24761.0 | 26625.0 | 12909.0 | 18193.0 | 39.1 | 0.4692 | 41687.0 | 24.8 | 23.464932 |
print(df_transform.dtypes)
county object state object GEOID object lon float64 lat float64 P_total float64 P_white float64 P_black float64 P_asian float64 P_native float64 P_hawaiian float64 P_others float64 P_below_middle_school float64 P_some_high_school float64 P_high_school_equivalent float64 P_some_college float64 P_bachelor_and_above float64 P_below_poverty float64 median_age float64 gini_index float64 median_household_income float64 median_rent_per_income float64 percent_no_internet float64 dtype: object
#create new column with GEOID manipulated to become FIPS codes for chloropleth
def func(row):
return row.GEOID[-5:]
df_transform['FIPS'] = df.apply(func,axis=1)
pd.set_option('display.max_columns', 24)
df_transform.head(5)
| county | state | GEOID | lon | lat | P_total | P_white | P_black | P_asian | P_native | P_hawaiian | P_others | P_below_middle_school | P_some_high_school | P_high_school_equivalent | P_some_college | P_bachelor_and_above | P_below_poverty | median_age | gini_index | median_household_income | median_rent_per_income | percent_no_internet | FIPS | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Anchorage Municipality | AK | 05000US02020 | -149.274354 | 61.177549 | 298192.0 | 184841.0 | 16102.0 | 27142.0 | 23916.0 | 7669.0 | 7935.0 | 2234.0 | 8196.0 | 44804.0 | 66162.0 | 70713.0 | 18302.0 | 33.0 | 0.4018 | 85634.0 | 28.0 | 6.593887 | 02020 |
| 1 | Fairbanks North Star Borough | AK | 05000US02090 | -146.599867 | 64.690832 | 100605.0 | 75501.0 | 4385.0 | 3875.0 | 7427.0 | 503.0 | 2357.0 | 924.0 | 1527.0 | 14725.0 | 24570.0 | 19257.0 | 9580.0 | 30.6 | 0.3756 | 77328.0 | 25.6 | 12.102458 | 02090 |
| 2 | Matanuska-Susitna Borough | AK | 05000US02170 | -149.407974 | 62.182173 | 104365.0 | 86314.0 | 1019.0 | 1083.0 | 5455.0 | 141.0 | 325.0 | 337.0 | 2755.0 | 21071.0 | 28472.0 | 12841.0 | 9893.0 | 34.2 | 0.4351 | 69332.0 | 29.6 | 11.156575 | 02170 |
| 3 | Baldwin County | AL | 05000US01003 | -87.746067 | 30.659218 | 208563.0 | 180484.0 | 18821.0 | 914.0 | 1383.0 | 0.0 | 1469.0 | 3245.0 | 10506.0 | 41822.0 | 46790.0 | 43547.0 | 23375.0 | 42.4 | 0.4498 | 56732.0 | 29.3 | 17.868167 | 01003 |
| 4 | Calhoun County | AL | 05000US01015 | -85.822513 | 33.771706 | 114611.0 | NaN | NaN | NaN | NaN | NaN | NaN | 2455.0 | 8853.0 | 24761.0 | 26625.0 | 12909.0 | 18193.0 | 39.1 | 0.4692 | 41687.0 | 24.8 | 23.464932 | 01015 |
print(df_transform.dtypes)
county object state object GEOID object lon float64 lat float64 P_total float64 P_white float64 P_black float64 P_asian float64 P_native float64 P_hawaiian float64 P_others float64 P_below_middle_school float64 P_some_high_school float64 P_high_school_equivalent float64 P_some_college float64 P_bachelor_and_above float64 P_below_poverty float64 median_age float64 gini_index float64 median_household_income float64 median_rent_per_income float64 percent_no_internet float64 FIPS object dtype: object
df_transform['FIPS'] = df_transform['FIPS'].apply(lambda x: '{0:0>5}'.format(x))
df_transform.head(5)
| county | state | GEOID | lon | lat | P_total | P_white | P_black | P_asian | P_native | P_hawaiian | P_others | P_below_middle_school | P_some_high_school | P_high_school_equivalent | P_some_college | P_bachelor_and_above | P_below_poverty | median_age | gini_index | median_household_income | median_rent_per_income | percent_no_internet | FIPS | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Anchorage Municipality | AK | 05000US02020 | -149.274354 | 61.177549 | 298192.0 | 184841.0 | 16102.0 | 27142.0 | 23916.0 | 7669.0 | 7935.0 | 2234.0 | 8196.0 | 44804.0 | 66162.0 | 70713.0 | 18302.0 | 33.0 | 0.4018 | 85634.0 | 28.0 | 6.593887 | 02020 |
| 1 | Fairbanks North Star Borough | AK | 05000US02090 | -146.599867 | 64.690832 | 100605.0 | 75501.0 | 4385.0 | 3875.0 | 7427.0 | 503.0 | 2357.0 | 924.0 | 1527.0 | 14725.0 | 24570.0 | 19257.0 | 9580.0 | 30.6 | 0.3756 | 77328.0 | 25.6 | 12.102458 | 02090 |
| 2 | Matanuska-Susitna Borough | AK | 05000US02170 | -149.407974 | 62.182173 | 104365.0 | 86314.0 | 1019.0 | 1083.0 | 5455.0 | 141.0 | 325.0 | 337.0 | 2755.0 | 21071.0 | 28472.0 | 12841.0 | 9893.0 | 34.2 | 0.4351 | 69332.0 | 29.6 | 11.156575 | 02170 |
| 3 | Baldwin County | AL | 05000US01003 | -87.746067 | 30.659218 | 208563.0 | 180484.0 | 18821.0 | 914.0 | 1383.0 | 0.0 | 1469.0 | 3245.0 | 10506.0 | 41822.0 | 46790.0 | 43547.0 | 23375.0 | 42.4 | 0.4498 | 56732.0 | 29.3 | 17.868167 | 01003 |
| 4 | Calhoun County | AL | 05000US01015 | -85.822513 | 33.771706 | 114611.0 | NaN | NaN | NaN | NaN | NaN | NaN | 2455.0 | 8853.0 | 24761.0 | 26625.0 | 12909.0 | 18193.0 | 39.1 | 0.4692 | 41687.0 | 24.8 | 23.464932 | 01015 |
print(df_transform.dtypes)
county object state object GEOID object lon float64 lat float64 P_total float64 P_white float64 P_black float64 P_asian float64 P_native float64 P_hawaiian float64 P_others float64 P_below_middle_school float64 P_some_high_school float64 P_high_school_equivalent float64 P_some_college float64 P_bachelor_and_above float64 P_below_poverty float64 median_age float64 gini_index float64 median_household_income float64 median_rent_per_income float64 percent_no_internet float64 FIPS object dtype: object
Now that the data is cleaned up, I wanted to take a quick look at internet access throughout the country. I thought this may be a way to assess my question regarding internet deserts.
#county chloropleth map of households without internet
colorscale = ["#f7fbff","#ebf3fb","#deebf7","#d2e3f3","#c6dbef","#b3d2e9","#9ecae1",
"#85bcdb","#6baed6","#57a0ce","#4292c6","#3082be","#2171b5","#1361a9",
"#08519c","#0b4083","#08306b"]
endpts = list(np.linspace(0, 55, len(colorscale) - 1))
fips = df_transform['FIPS'].tolist()
values = df_transform['percent_no_internet'].tolist()
fig = ff.create_choropleth(
fips=fips, values=values,
binning_endpoints=endpts,
colorscale=colorscale,
show_state_data=False,
show_hover=True, centroid_marker={'opacity': 0},
asp=2.9, title='Percentage of People with No Internet',
legend_title='% without internet'
)
iplot(fig, filename='choropleth_full_usa')
I used a Chloropleth map here to gain a quick visual, and confirm or deny my initial thoughts about the dataset. This guided my code, because as you’ll see above there are some counties with very dark blue sections to indicate lack of internet access. I was immediately intrigued by the situation in Apache County, Arizona with 54% of people without internet. It also shows how much of the data was excluded or unavailable. This may make the data we have helpful if there are strong correlations between the datapoints in it.
#state cholorpleth of households without internet
import plotly.plotly as py
import pandas as pd
df_transform
for col in df.columns:
df[col] = df[col].astype(str)
scl = [[0.0, 'rgb(242,240,247)'],[0.2, 'rgb(218,218,235)'],[0.4, 'rgb(188,189,220)'],\
[0.6, 'rgb(158,154,200)'],[0.8, 'rgb(117,107,177)'],[1.0, 'rgb(84,39,143)']]
df['text'] = df['state'] + '<br>' +\
'Median Household Income '+df['median_household_income']+' Median Age '+df['median_age']+'<br>'+\
'Below Poverty '+df['P_below_poverty']+' Without Internet ' + df['percent_no_internet']
data = [ dict(
type='choropleth',
colorscale = scl,
autocolorscale = False,
locations = df['state'],
z = df['percent_no_internet'].astype(float),
locationmode = 'USA-states',
text = df['text'],
marker = dict(
line = dict (
color = 'rgb(255,255,255)',
width = 2
) ),
colorbar = dict(
title = "% of Homes Without Internet")
) ]
layout = dict(
title = 'Homes Without Internet by State',
geo = dict(
scope='usa',
projection=dict( type='albers usa' ),
showlakes = True,
lakecolor = 'rgb(255, 255, 255)'),
)
fig = dict( data=data, layout=layout )
iplot( fig, filename='d3-cloropleth-map' )
I also used a state chloropleth map to see if there were entire states that might have less access to internet, and begin to get a good idea of where “internet deserts” might be in the US. As you see here, the South, and Midwestern United States have many populations that do not have access to internet.
#for reference I've calculated the mean, and standard deviations, so we can learn what is statistically significant.
#this still includes outliers like Apache County, AZ
mean = df_transform.percent_no_internet.mean()
print('Statistics for Percent of Household Without Internet Access:')
print('Mean:')
print(mean)
variance = df_transform.percent_no_internet.var()
print('Variance:')
print(variance)
std_dev = df_transform.percent_no_internet.std()
print('Standard Deviation:')
print(std_dev)
one_above = (mean + std_dev)
print('One Standard Deviation Above the Mean:')
print(one_above)
three_above = (mean + (std_dev * 3))
print('Three Standard Deviations Above the Mean')
print(three_above)
one_below = (mean - std_dev)
print('One Standard Deviation Below the Mean:')
print(one_below)
three_below = (mean - (std_dev * 3))
print('Three Standard Deviations Below the Mean:')
print(three_below)
Statistics for Percent of Household Without Internet Access: Mean: 15.264665232867896 Variance: 34.66452572836923 Standard Deviation: 5.887658764599832 One Standard Deviation Above the Mean: 21.15232399746773 Three Standard Deviations Above the Mean 32.927641526667394 One Standard Deviation Below the Mean: 9.377006468268064 Three Standard Deviations Below the Mean: -2.398311060931599
To better understand the data, I needed to know what the mean was, and when data started to be really significant for my analysis. What amount of people without internet was exceptionally high? What amount indicated that the county had very good internet access? With the mean at about 15% of the county without internet access, it started to paint a better picture of what it meant to have exceptionally high or low access. It is worth noting that the dataset has high variance. While this is not necessarily a statistical problem, it does show the inequity of internet access in the United States.
df_transform = df_transform[df_transform.percent_no_internet < 32.928]
df_transform.head(5)
| county | state | GEOID | lon | lat | P_total | P_white | P_black | P_asian | P_native | P_hawaiian | P_others | P_below_middle_school | P_some_high_school | P_high_school_equivalent | P_some_college | P_bachelor_and_above | P_below_poverty | median_age | gini_index | median_household_income | median_rent_per_income | percent_no_internet | FIPS | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Anchorage Municipality | AK | 05000US02020 | -149.274354 | 61.177549 | 298192.0 | 184841.0 | 16102.0 | 27142.0 | 23916.0 | 7669.0 | 7935.0 | 2234.0 | 8196.0 | 44804.0 | 66162.0 | 70713.0 | 18302.0 | 33.0 | 0.4018 | 85634.0 | 28.0 | 6.593887 | 02020 |
| 1 | Fairbanks North Star Borough | AK | 05000US02090 | -146.599867 | 64.690832 | 100605.0 | 75501.0 | 4385.0 | 3875.0 | 7427.0 | 503.0 | 2357.0 | 924.0 | 1527.0 | 14725.0 | 24570.0 | 19257.0 | 9580.0 | 30.6 | 0.3756 | 77328.0 | 25.6 | 12.102458 | 02090 |
| 2 | Matanuska-Susitna Borough | AK | 05000US02170 | -149.407974 | 62.182173 | 104365.0 | 86314.0 | 1019.0 | 1083.0 | 5455.0 | 141.0 | 325.0 | 337.0 | 2755.0 | 21071.0 | 28472.0 | 12841.0 | 9893.0 | 34.2 | 0.4351 | 69332.0 | 29.6 | 11.156575 | 02170 |
| 3 | Baldwin County | AL | 05000US01003 | -87.746067 | 30.659218 | 208563.0 | 180484.0 | 18821.0 | 914.0 | 1383.0 | 0.0 | 1469.0 | 3245.0 | 10506.0 | 41822.0 | 46790.0 | 43547.0 | 23375.0 | 42.4 | 0.4498 | 56732.0 | 29.3 | 17.868167 | 01003 |
| 4 | Calhoun County | AL | 05000US01015 | -85.822513 | 33.771706 | 114611.0 | NaN | NaN | NaN | NaN | NaN | NaN | 2455.0 | 8853.0 | 24761.0 | 26625.0 | 12909.0 | 18193.0 | 39.1 | 0.4692 | 41687.0 | 24.8 | 23.464932 | 01015 |
trace0 = go.Box(
y=df_transform.percent_no_internet,
name='Mean & SD',
marker=dict(
color='rgb(10, 140, 208)',
),
boxmean='sd'
)
data = [trace0]
layout = go.Layout(
title='Households Without Internet Boxplot',
yaxis=dict(
title='Percent of Households with No Internet',
titlefont=dict(
family='Courier New, monospace',
size=18,
color='#7f7f7f'
)
)
)
fig = dict(data=data, layout=layout)
iplot(fig, filename='boxplot')
#for reference I've calculated the mean, and standard deviations, so we can learn what is statistically significant.
#this still includes outliers like Apache County, AZ
mean = df_transform.percent_no_internet.mean()
print('Statistics for Percent of Household Without Internet Access:')
print('Mean:')
print(mean)
variance = df_transform.percent_no_internet.var()
print('Variance:')
print(variance)
std_dev = df_transform.percent_no_internet.std()
print('Standard Deviation:')
print(std_dev)
one_above = (mean + std_dev)
print('One Standard Deviation Above the Mean:')
print(one_above)
three_above = (mean + (std_dev * 3))
print('Three Standard Deviations Above the Mean')
print(three_above)
one_below = (mean - std_dev)
print('One Standard Deviation Below the Mean:')
print(one_below)
three_below = (mean - (std_dev * 3))
print('Three Standard Deviations Below the Mean:')
print(three_below)
Statistics for Percent of Household Without Internet Access: Mean: 15.017807864600915 Variance: 28.302371741718005 Standard Deviation: 5.319997344145766 One Standard Deviation Above the Mean: 20.33780520874668 Three Standard Deviations Above the Mean 30.977799897038214 One Standard Deviation Below the Mean: 9.69781052045515 Three Standard Deviations Below the Mean: -0.942184167836384
With exceptional outliers outside of two standard deviations above the mean excluded, I wanted to see how this impacted the statistics. While the changes were not drastic, I still think it was worth excluding counties where more than 32% of households do not have internet since there may be some extenuating circumstances that are not representative of the dataset.
x = df_transform.percent_no_internet
data = [go.Histogram(x=x)]
iplot(data, filename='basic histogram')
Since the data was fairly normally distributed, I did not normalize the data any further, and worked with it as is.
data = [go.Bar(x=df_transform.state,
y=df_transform.percent_no_internet)]
layout = go.Layout(
title='Households Without Internet by State',
xaxis=dict(
title='States',
titlefont=dict(
family='Courier New, monospace',
size=18,
color='#7f7f7f'
)
),
yaxis=dict(
title='Percent of Households with No Internet',
titlefont=dict(
family='Courier New, monospace',
size=18,
color='#7f7f7f'
)
)
)
fig = dict(data=data, layout=layout)
iplot(fig, filename='basic-bar')
This is one last graph for initial understanding. States such as Arizona and Texas have high rates of households without internet access. Texas is very interesting given it's high level of variance. The mean access is low, but there are clearly areas that have many households without access. It is worth noting that a few counties in Texas were excluded from the data since they may be outliers.
After doing an initial analysis of the mean and understanding what communities have many households without internet, I was left to think about why this might be. This does indeed show that we have a wide variance of internet access in the United States, but also a dearth of data in this area. Given the data available, I think income, poverty rates and education might be related to especially high or low rates of households without internet access. To best analyze it, I'll run linear regressions where appropriate, so that it might give us clues as to what to expect from data that we may not have through this dataset, given it's natural limitations.
xi = df_transform.median_household_income
y = df_transform.percent_no_internet
# Generated linear fit
slope, intercept, r_value, p_value, std_err = stats.linregress(xi,y)
line = slope*xi+intercept
# Creating the dataset, and generating the plot
trace1 = go.Scatter(
x=xi,
y=y,
text = df_transform.county +', ' + df_transform.state,
mode='markers',
marker=go.Marker(color='rgb(255, 127, 14)'),
name='Data'
)
trace2 = go.Scatter(
x=xi,
y=line,
mode='lines',
marker=go.Marker(color='rgb(31, 119, 180)'),
name='Fit'
)
annotation = go.Annotation(
x=10,# Does this stay constant?
y=80000,#Does this stay constant?
text='$R^2 = 0.9551,\\Y = 0.716X + 19.18$',
showarrow=False,
font=go.Font(size=16)
)
data = [trace1, trace2]
layout = go.Layout(
title='Percentage of Households Without Internet and Median Income',
xaxis=dict(
title='Median Income',
titlefont=dict(
family='Courier New, monospace',
size=18,
color='#7f7f7f'
)
),
yaxis=dict(
title='Percentage of Households Without Internet',
titlefont=dict(
family='Courier New, monospace',
size=18,
color='#7f7f7f'
)
)
)
fig = go.Figure(data=data, layout=layout)
iplot(fig, filename='Linear-Fit-in-python')
print('Slope:')
print(slope)
print('Amount Households Without Internet Decreases Per $10,000' )
print(slope * 10000)
/anaconda3/lib/python3.6/site-packages/plotly/graph_objs/_deprecations.py:426: DeprecationWarning: plotly.graph_objs.Marker is deprecated. Please replace it with one of the following more specific types - plotly.graph_objs.scatter.Marker - plotly.graph_objs.histogram.selected.Marker - etc. /anaconda3/lib/python3.6/site-packages/plotly/graph_objs/_deprecations.py:318: DeprecationWarning: plotly.graph_objs.Font is deprecated. Please replace it with one of the following more specific types - plotly.graph_objs.layout.Font - plotly.graph_objs.layout.hoverlabel.Font - etc. /anaconda3/lib/python3.6/site-packages/plotly/graph_objs/_deprecations.py:144: DeprecationWarning: plotly.graph_objs.Annotation is deprecated. Please replace it with one of the following more specific types - plotly.graph_objs.layout.Annotation - plotly.graph_objs.layout.scene.Annotation
Slope: -0.0002544184703634143 Amount Households Without Internet Decreases Per $10,000 -2.544184703634143
This was a very clear relationship in the data. With the dependent variable, Percent of Households with No Internet, on the Y-Axis, we can see that as a county's median household income increases, it's number of households without internet decreases. I also ran a linear regression, which showed good fit and affirmed the inverse relationship. This is logical since households will have more disposable income that they can use to subscribe to broadband internet.
trace0 = go.Scatter(
x = df_transform.median_age,
y = df_transform.percent_no_internet,
name = 'Above',
text = df_transform.county +', ' + df_transform.state,
mode = 'markers',
marker = dict(
size = 10,
color = 'rgba(63, 191, 191, .8)',
line = dict(
width = 2,
color = 'rgb(0, 0, 0)'
)
)
)
data = [trace0]
layout = go.Layout(
title='Percentage of Households Without Internet and Median Age',
xaxis=dict(
title='Median Age',
titlefont=dict(
family='Courier New, monospace',
size=18,
color='#7f7f7f'
)
),
yaxis=dict(
title='Percent of Households with No Internet',
titlefont=dict(
family='Courier New, monospace',
size=18,
color='#7f7f7f'
)
)
)
fig = go.Figure(data=data, layout=layout)
iplot(fig, filename='basic-scatter')
This dataset did show a strong relationship between median age and percentage of households without internet. I had expected that as age increased, households without internet may increase since older generations may have less interest in using broadband internet.
I thought that education level might have a good deal of influence on the percentage of houses without internet in a particular county. First, I created a percentage of the county that had a particular education level by using the population total (P_total) and the number of people with each education level for the counties for which I had data. I ran an initial scatterplot to see if what I posited might be correct.
percent_below_middle = (df_transform.P_below_middle_school/df_transform.P_total *100)
percent_some_hs = (df_transform.P_some_high_school/df_transform.P_total * 100)
percent_hs = (df_transform.P_high_school_equivalent/df_transform.P_total * 100)
percent_some_col = (df_transform.P_some_college/df_transform.P_total * 100)
percent_bach_or_above = (df_transform.P_bachelor_and_above/df_transform.P_total * 100)
trace0 = go.Scatter(
x = percent_below_middle,
y = df_transform.percent_no_internet,
name = 'Below Middle School',
text = df_transform.county +', ' + df_transform.state,
mode = 'markers',
marker = dict(
size = 10,
color = 'rgb(86, 244, 66, .8)',
line = dict(
width = 2,
color = 'rgb(0, 0, 0)'
)
)
)
trace1 = go.Scatter(
x = percent_some_hs,
y = df_transform.percent_no_internet,
name = 'Some High School',
text = df_transform.county +', ' + df_transform.state,
mode = 'markers',
marker = dict(
size = 10,
color = 'rgb(65, 244, 223, .8)',
line = dict(
width = 2,
)
)
)
trace2 = go.Scatter(
x = percent_hs,
y = df_transform.percent_no_internet,
name = 'High School Degree/Equivalent',
text = df_transform.county +', ' + df_transform.state,
mode = 'markers',
marker = dict(
size = 10,
color = 'rgb(202, 65, 244, .8)',
line = dict(
width = 2,
)
)
)
trace3 = go.Scatter(
x = percent_some_col,
y = df_transform.percent_no_internet,
name = 'Some College',
text = df_transform.county +', ' + df_transform.state,
mode = 'markers',
marker = dict(
size = 10,
color = 'rgb(244, 178, 65, .8)',
line = dict(
width = 2,
)
)
)
trace4 = go.Scatter(
x = percent_bach_or_above,
y = df_transform.percent_no_internet,
name = 'Bachelor and Above',
text = df_transform.county +', ' + df_transform.state,
mode = 'markers',
marker = dict(
size = 10,
color = 'rgb(241, 244, 65, .8)',
line = dict(
width = 2,
)
)
)
data = [trace0, trace1, trace2, trace3, trace4]
layout = go.Layout(
title='Education Level and Percent No Internet',
xaxis=dict(
title='Percentage of People at Education Level',
titlefont=dict(
family='Courier New, monospace',
size=18,
color='#7f7f7f'
)
),
yaxis=dict(
title='% Households Without Internet',
titlefont=dict(
family='Courier New, monospace',
size=18,
color='#7f7f7f'
)
)
)
fig = dict(data=data, layout=layout)
iplot(fig, filename='styled-scatter')
Education does appear to have an impact on internet access. There are strong relationships between bachelor's degrees, and people who have not obtained a high school degree, or completed middle school. I have run regressions on those scatterplots to make sure that they fit well with a linear regression. This could be problematic in the future because it could contribute to a household's inability to progress in the education system, since a student's lack of internet access can be detrimental to their educational progress according to this article from Pew Research.
percent_below_middle = (df_transform.P_below_middle_school/df_transform.P_total *100)
xi = percent_below_middle
y = df_transform.percent_no_internet
# Generated linear fit
mask = ~np.isnan(xi) & ~np.isnan(y)
slope, intercept, r_value, p_value, std_err = stats.linregress(xi[mask], y[mask])
line = slope*xi+intercept
# Creating the dataset, and generating the plot
trace1 = go.Scatter(
x=xi,
y=y,
text = df_transform.county +', ' + df_transform.state,
mode='markers',
marker=go.Marker(color='rgb(86, 244, 66, .8)'),
name='Data'
)
trace2 = go.Scatter(
x=xi,
y=line,
mode='lines',
marker=go.Marker(color='rgb(31, 119, 180)'),
name='Fit'
)
data = [trace1, trace2]
layout = go.Layout(
title='Percentage of Households Without Internet and Percentage Who Did Not Complete Middle School',
xaxis=dict(
title='Percentage of People Who Did Not Complete Middle School',
titlefont=dict(
family='Courier New, monospace',
size=18,
color='#7f7f7f'
)
),
yaxis=dict(
title='Percent of Households Without Internet',
titlefont=dict(
family='Courier New, monospace',
size=18,
color='#7f7f7f'
)
)
)
fig = go.Figure(data=data, layout=layout)
iplot(fig, filename='Linear-Fit-in-python')
/anaconda3/lib/python3.6/site-packages/plotly/graph_objs/_deprecations.py:426: DeprecationWarning: plotly.graph_objs.Marker is deprecated. Please replace it with one of the following more specific types - plotly.graph_objs.scatter.Marker - plotly.graph_objs.histogram.selected.Marker - etc.
This did not correlate as strongly as I thought it would. However, it does indicate that generally as the percentage of people who did not complete middle school may increase the likelihood that the percentage of households without internet will increase.
percent_some_hs = (df_transform.P_some_high_school/df_transform.P_total * 100)
xi = percent_some_hs
y = df_transform.percent_no_internet
# Generated linear fit
mask = ~np.isnan(xi) & ~np.isnan(y)
slope, intercept, r_value, p_value, std_err = stats.linregress(xi[mask], y[mask])
line = slope*xi+intercept
# Creating the dataset, and generating the plot
trace1 = go.Scatter(
x=xi,
y=y,
text = df_transform.county +', ' + df_transform.state,
mode='markers',
marker=go.Marker(color='rgb(113, 21, 211, .8)'),
name='Data'
)
trace2 = go.Scatter(
x=xi,
y=line,
mode='lines',
marker=go.Marker(color='rgb(21, 210, 33)'),
name='Fit'
)
data = [trace1, trace2]
layout = go.Layout(
title='Percentage of Households Without Internet and Percentage Who Did Not Complete High School',
xaxis=dict(
title='Percentage of People Who Did Not Complete High School',
titlefont=dict(
family='Courier New, monospace',
size=18,
color='#7f7f7f'
)
),
yaxis=dict(
title='Percent of Households Without Internet',
titlefont=dict(
family='Courier New, monospace',
size=18,
color='#7f7f7f'
)
)
)
fig = go.Figure(data=data, layout=layout)
iplot(fig, filename='Linear-Fit-in-python')
print('Mean of People Who Did Not Complete High School:')
print(percent_some_hs.mean())
/anaconda3/lib/python3.6/site-packages/plotly/graph_objs/_deprecations.py:426: DeprecationWarning: plotly.graph_objs.Marker is deprecated. Please replace it with one of the following more specific types - plotly.graph_objs.scatter.Marker - plotly.graph_objs.histogram.selected.Marker - etc.
Mean of People Who Did Not Complete High School: 4.7644457401609355 Slope: 2.018361916016112
This graph shows a direct relationship between people who have some high school education, and the percentage of the county without internet access. The scatterplot is tightly arranged around the linear regression, and could be helpful for data prediction and analysis as we consider counties that have higher populations of people who did not complete high school. Since the mean is about 4%, counties that have population data, but no data regarding internet access could be analyzed further. Until more data is collected we can't be sure, but it's a strong possibility that counties that have more that 4% of people who did not complete high school would have less internet access.
percent_bach_or_above = (df_transform.P_bachelor_and_above/df_transform.P_total * 100)
xi = percent_bach_or_above
y = df_transform.percent_no_internet
# Generated linear fit
mask = ~np.isnan(xi) & ~np.isnan(y)
slope, intercept, r_value, p_value, std_err = stats.linregress(xi[mask], y[mask])
line = slope*xi+intercept
# Creating the dataset, and generating the plot
trace1 = go.Scatter(
x=xi,
y=y,
text = df_transform.county +', ' + df_transform.state,
mode='markers',
marker=go.Marker(color='rgb(205, 244, 65, .8)'),
name='Data'
)
trace2 = go.Scatter(
x=xi,
y=line,
mode='lines',
marker=go.Marker(color='rgb(54, 56, 49)'),
name='Fit'
)
data = [trace1, trace2]
layout = go.Layout(
title='Percentage of Households Without Internet and Percentage of People With Bachelors Degrees and Above',
xaxis=dict(
title='Percentage of People Who Have A Bachelors Degree or Above',
titlefont=dict(
family='Courier New, monospace',
size=18,
color='#7f7f7f'
)
),
yaxis=dict(
title='Percent of Households Without Internet',
titlefont=dict(
family='Courier New, monospace',
size=18,
color='#7f7f7f'
)
)
)
fig = go.Figure(data=data, layout=layout)
iplot(fig, filename='Linear-Fit-in-python')
print('Mean Percentage of Those with Bachelors Degrees or More Advanced Degrees:')
print(percent_bach_or_above.mean())
/anaconda3/lib/python3.6/site-packages/plotly/graph_objs/_deprecations.py:426: DeprecationWarning: plotly.graph_objs.Marker is deprecated. Please replace it with one of the following more specific types - plotly.graph_objs.scatter.Marker - plotly.graph_objs.histogram.selected.Marker - etc.
Mean Percentage of Those with Bachelors Degrees or More Advanced Degrees: 19.75133498857654
This shows a strong relationship with good linear fit. As the percentage of people who have a bachelor's degree or more advanced degree increases, the percentage of the county without internet decreases. Nearly 40% of the county with the lowest percentage of households without internet, Douglas County in Colorado, have a bachelor's degree or higher. Perhaps this might be related to colleges or universities being nearby, which typically have excellent internet access and generally pays employees well, so that they might be able to afford broadband internet access. The mean of this data is about 19.6%, which could help with predictive data in the future. In counties where more than 19.6% of the population has a bachelor's degree could have exceptionally good internet, and may have structures in place that are worth analyzing. While it may just be good infrastructure, there may be other factors that could be replicated in places that do not have high rates of internet access.
# Create a trace
trace = go.Scatter(
x = df_transform.median_rent_per_income,
y = df_transform.percent_no_internet,
name = 'Below',
text = df_transform.county +', ' + df_transform.state,
mode = 'markers',
marker = dict(
size = 10,
color = 'rgb(206, 16, 168, .8)',
line = dict(
width = 2,
)
)
)
data = [trace]
layout = go.Layout(
title='Percentage of Households Compared to Percent Spent on Rent',
xaxis=dict(
title='Percent of Income Spent on Rent',
titlefont=dict(
family='Courier New, monospace',
size=18,
color='#7f7f7f'
)
),
yaxis=dict(
title='Percent of Households with No Internet',
titlefont=dict(
family='Courier New, monospace',
size=18,
color='#7f7f7f'
)
)
)
fig = dict(data=data, layout=layout)
iplot(fig, filename='styled-scatter')
In short, it does not appear so. I had expected this data to show a stronger, inverse relationship. However, percent of income spent on rent did not have a great impact on the percent of households without internet. However, this still does make sense as it is reasonable that people may move to a more expensive area if they make more money, rather than spend less of their income in an area that is less expensive.
I first converted the column P_below_poverty to a percentage, so that the data could be properly analyzed. I then generated a scatterplot so I could assess the relationship I posited.
percent_below_poverty = (df_transform.P_below_poverty/df_transform.P_total * 100)
xi = percent_below_poverty
y = df_transform.percent_no_internet
# Generated linear fit
mask = ~np.isnan(xi) & ~np.isnan(y)
slope, intercept, r_value, p_value, std_err = stats.linregress(xi[mask], y[mask])
line = slope*xi+intercept
# Creating the dataset, and generating the plot
trace1 = go.Scatter(
x=xi,
y=y,
text = df_transform.county +', ' + df_transform.state,
mode='markers',
marker=go.Marker(color='rgb(15, 216, 193, .1)'),
name='Data'
)
trace2 = go.Scatter(
x=xi,
y=line,
mode='lines',
marker=go.Marker(color='rgb(31, 119, 180)'),
name='Fit'
)
annotation = go.Annotation(
x=3.5,# Does this stay constant?
y=23.5,#Does this stay constant?
text='$R^2 = 0.9551,\\Y = 0.716X + 19.18$',
showarrow=False,
font=go.Font(size=16)
)
data = [trace1, trace2]
layout = go.Layout(
title='Percentage of Households Without Internet and Percentage of Households Below Poverty',
xaxis=dict(
title='Percent of Households Below Poverty',
titlefont=dict(
family='Courier New, monospace',
size=18,
color='#7f7f7f'
)
),
yaxis=dict(
title='Percent of County Without Internet',
titlefont=dict(
family='Courier New, monospace',
size=18,
color='#7f7f7f'
)
)
)
fig = go.Figure(data=data, layout=layout)
iplot(fig, filename='Linear-Fit-in-python')
/anaconda3/lib/python3.6/site-packages/plotly/graph_objs/_deprecations.py:426: DeprecationWarning: plotly.graph_objs.Marker is deprecated. Please replace it with one of the following more specific types - plotly.graph_objs.scatter.Marker - plotly.graph_objs.histogram.selected.Marker - etc. /anaconda3/lib/python3.6/site-packages/plotly/graph_objs/_deprecations.py:318: DeprecationWarning: plotly.graph_objs.Font is deprecated. Please replace it with one of the following more specific types - plotly.graph_objs.layout.Font - plotly.graph_objs.layout.hoverlabel.Font - etc. /anaconda3/lib/python3.6/site-packages/plotly/graph_objs/_deprecations.py:144: DeprecationWarning: plotly.graph_objs.Annotation is deprecated. Please replace it with one of the following more specific types - plotly.graph_objs.layout.Annotation - plotly.graph_objs.layout.scene.Annotation
It does appear that the percentage of households below poverty did relate positively to the percentage of households without internet. This makes sense, since someone who earns an income below the rate of poverty would reasonably not be able to afford expensive internet plans. This could have an impact in the future, since we are very internet reliant as a society currently and will only grow to be more so. This may be an important consideration, if this data analysis holds true, for public policy decisions in the future. Just as we have assistance for people who have an income below a certain point for food, housing, and heat, internet may just become that important to society and may be something we need to adjust for in future social protections.
#data manipulation
percent_pop_white = (df_transform.P_white/df_transform.P_total * 100)
percent_pop_black = (df_transform.P_black/df_transform.P_total * 100)
percent_pop_asian = (df_transform.P_asian/df_transform.P_total * 100)
percent_pop_hawaiian = (df_transform.P_hawaiian/df_transform.P_total * 100)
percent_pop_natamer = (df_transform.P_native/df_transform.P_total *100)
percent_pop_other = (df_transform.P_others/df_transform.P_total *100)
#traces
trace0 = go.Scatter(
x = percent_pop_white,
y = df_transform.percent_no_internet,
name = 'White',
text = df_transform.county +', ' + df_transform.state,
mode = 'markers',
marker = dict(
size = 10,
color = 'rgb(58, 124, 153, .8)',
line = dict(
width = 2,
color = 'rgb(0, 0, 0)'
)
)
)
trace1 = go.Scatter(
x = percent_pop_black,
y = df_transform.percent_no_internet,
name = 'Black',
text = df_transform.county +', ' + df_transform.state,
mode = 'markers',
marker = dict(
size = 10,
color = 'rgb(254, 127, 45, .8)',
line = dict(
width = 2,
)
)
)
trace2 = go.Scatter(
x = percent_pop_asian,
y = df_transform.percent_no_internet,
name = 'Asian',
text = df_transform.county +', ' + df_transform.state,
mode = 'markers',
marker = dict(
size = 10,
color = 'rgb(202, 69, 252, .8)',
line = dict(
width = 2,
)
)
)
trace3 = go.Scatter(
x = percent_pop_hawaiian,
y = df_transform.percent_no_internet,
name = 'Hawaiian',
text = df_transform.county +', ' + df_transform.state,
mode = 'markers',
marker = dict(
size = 10,
color = 'rgb(87, 156, 1135, .8)',
line = dict(
width = 2,
)
)
)
trace4 = go.Scatter(
x = percent_pop_natamer,
y = df_transform.percent_no_internet,
name = 'Native American',
text = df_transform.county +', ' + df_transform.state,
mode = 'markers',
marker = dict(
size = 10,
color = 'rgb(241, 244, 65, .8)',
line = dict(
width = 2,
)
)
)
trace5 = go.Scatter(
x = percent_pop_other,
y = df_transform.percent_no_internet,
name = 'Other',
text = df_transform.county +', ' + df_transform.state,
mode = 'markers',
marker = dict(
size = 10,
color = 'rgb(116, 193, 65, .8)',
line = dict(
width = 2,
)
)
)
data = [trace0, trace1, trace2, trace3, trace4, trace5]
layout = go.Layout(
title='Race and Percent No Internet',
xaxis=dict(
title='# of People by Race',
titlefont=dict(
family='Courier New, monospace',
size=18,
color='#7f7f7f'
)
),
yaxis=dict(
title='Percent of Households with No Internet',
titlefont=dict(
family='Courier New, monospace',
size=18,
color='#7f7f7f'
)
)
)
fig = dict(data=data, layout=layout)
iplot(fig, filename='styled-scatter')
I did not anticipate a strong relationship here, and this seems to be supported by the data. I generated the percentage of people by each race, and related that to the percentage of households without internet. This would not be helpful in assessing this data, or trying to predict where internet access rates might be higher or lower. Additionaly, these columns were some of the more incomplete ones in the dataset. There may be stronger relationships here, but I am unable to analyze it with the current data available.
To build and fill the Chloropleth map from the start of the data, I had to find a dataset with FIPS codes, and then clean it so that it could be used here. This included adding some leading zeroes, and concatenating two of the columns before finally merging it with the dataframe I used for the rest of the dataset.
df_1 = pd.read_csv('~/Desktop/Python Exercises/us_fips_codes.csv')
df_1.head(5)
| State | County Name | FIPS State | FIPS County | |
|---|---|---|---|---|
| 0 | Alabama | Autauga | 1 | 1 |
| 1 | Alabama | Baldwin | 1 | 3 |
| 2 | Alabama | Barbour | 1 | 5 |
| 3 | Alabama | Bibb | 1 | 7 |
| 4 | Alabama | Blount | 1 | 9 |
print(df_1.dtypes)
State object County Name object FIPS State int64 FIPS County int64 dtype: object
df_1['FIPS State'] = df_1['FIPS State'].apply(lambda x: '{0:0>2}'.format(x))
df_1
| State | County Name | FIPS State | FIPS County | |
|---|---|---|---|---|
| 0 | Alabama | Autauga | 01 | 1 |
| 1 | Alabama | Baldwin | 01 | 3 |
| 2 | Alabama | Barbour | 01 | 5 |
| 3 | Alabama | Bibb | 01 | 7 |
| 4 | Alabama | Blount | 01 | 9 |
| 5 | Alabama | Bullock | 01 | 11 |
| 6 | Alabama | Butler | 01 | 13 |
| 7 | Alabama | Calhoun | 01 | 15 |
| 8 | Alabama | Chambers | 01 | 17 |
| 9 | Alabama | Cherokee | 01 | 19 |
| 10 | Alabama | Chilton | 01 | 21 |
| 11 | Alabama | Choctaw | 01 | 23 |
| 12 | Alabama | Clarke | 01 | 25 |
| 13 | Alabama | Clay | 01 | 27 |
| 14 | Alabama | Cleburne | 01 | 29 |
| 15 | Alabama | Coffee | 01 | 31 |
| 16 | Alabama | Colbert | 01 | 33 |
| 17 | Alabama | Conecuh | 01 | 35 |
| 18 | Alabama | Coosa | 01 | 37 |
| 19 | Alabama | Covington | 01 | 39 |
| 20 | Alabama | Crenshaw | 01 | 41 |
| 21 | Alabama | Cullman | 01 | 43 |
| 22 | Alabama | Dale | 01 | 45 |
| 23 | Alabama | Dallas | 01 | 47 |
| 24 | Alabama | De Kalb | 01 | 49 |
| 25 | Alabama | Elmore | 01 | 51 |
| 26 | Alabama | Escambia | 01 | 53 |
| 27 | Alabama | Etowah | 01 | 55 |
| 28 | Alabama | Fayette | 01 | 57 |
| 29 | Alabama | Franklin | 01 | 59 |
| 30 | Alabama | Geneva | 01 | 61 |
| 31 | Alabama | Greene | 01 | 63 |
| 32 | Alabama | Hale | 01 | 65 |
| 33 | Alabama | Henry | 01 | 67 |
| 34 | Alabama | Houston | 01 | 69 |
| 35 | Alabama | Jackson | 01 | 71 |
| 36 | Alabama | Jefferson | 01 | 73 |
| 37 | Alabama | Lamar | 01 | 75 |
| 38 | Alabama | Lauderdale | 01 | 77 |
| 39 | Alabama | Lawrence | 01 | 79 |
| 40 | Alabama | Lee | 01 | 81 |
| 41 | Alabama | Limestone | 01 | 83 |
| 42 | Alabama | Lowndes | 01 | 85 |
| 43 | Alabama | Macon | 01 | 87 |
| 44 | Alabama | Madison | 01 | 89 |
| 45 | Alabama | Marengo | 01 | 91 |
| 46 | Alabama | Marion | 01 | 93 |
| 47 | Alabama | Marshall | 01 | 95 |
| 48 | Alabama | Mobile | 01 | 97 |
| 49 | Alabama | Monroe | 01 | 99 |
| 50 | Alabama | Montgomery | 01 | 101 |
| 51 | Alabama | Morgan | 01 | 103 |
| 52 | Alabama | Perry | 01 | 105 |
| 53 | Alabama | Pickens | 01 | 107 |
| 54 | Alabama | Pike | 01 | 109 |
| 55 | Alabama | Randolph | 01 | 111 |
| 56 | Alabama | Russell | 01 | 113 |
| 57 | Alabama | St Clair | 01 | 115 |
| 58 | Alabama | Shelby | 01 | 117 |
| 59 | Alabama | Sumter | 01 | 119 |
| 60 | Alabama | Talladega | 01 | 121 |
| 61 | Alabama | Tallapoosa | 01 | 123 |
| 62 | Alabama | Tuscaloosa | 01 | 125 |
| 63 | Alabama | Walker | 01 | 127 |
| 64 | Alabama | Washington | 01 | 129 |
| 65 | Alabama | Wilcox | 01 | 131 |
| 66 | Alabama | Winston | 01 | 133 |
| 67 | Alaska | Aleutians East | 02 | 13 |
| 68 | Alaska | Aleutians West | 02 | 16 |
| 69 | Alaska | Anchorage | 02 | 20 |
| 70 | Alaska | Bethel | 02 | 50 |
| 71 | Alaska | Bristol Bay | 02 | 60 |
| 72 | Alaska | Denali | 02 | 68 |
| 73 | Alaska | Dillingham | 02 | 70 |
| 74 | Alaska | Fairbanks North Star | 02 | 90 |
| 75 | Alaska | Haines | 02 | 100 |
| 76 | Alaska | Juneau | 02 | 110 |
| 77 | Alaska | Kenai Peninsula | 02 | 122 |
| 78 | Alaska | Ketchikan Gateway | 02 | 130 |
| 79 | Alaska | Kodiak Island | 02 | 150 |
| 80 | Alaska | Lake and Peninsula | 02 | 164 |
| 81 | Alaska | Matanuska Susitna | 02 | 170 |
| 82 | Alaska | Nome | 02 | 180 |
| 83 | Alaska | North Slope | 02 | 185 |
| 84 | Alaska | Northwest Arctic | 02 | 188 |
| 85 | Alaska | Prince Wales Ketchikan | 02 | 201 |
| 86 | Alaska | Sitka | 02 | 220 |
| 87 | Alaska | Skagway Hoonah Angoon | 02 | 232 |
| 88 | Alaska | Southeast Fairbanks | 02 | 240 |
| 89 | Alaska | Valdez Cordova | 02 | 261 |
| 90 | Alaska | Wade Hampton | 02 | 270 |
| 91 | Alaska | Wrangell Petersburg | 02 | 280 |
| 92 | Alaska | Yakutat | 02 | 282 |
| 93 | Alaska | Yukon Koyukuk | 02 | 290 |
| 94 | Arizona | Apache | 04 | 1 |
| 95 | Arizona | Cochise | 04 | 3 |
| 96 | Arizona | Coconino | 04 | 5 |
| 97 | Arizona | Gila | 04 | 7 |
| 98 | Arizona | Graham | 04 | 9 |
| 99 | Arizona | Greenlee | 04 | 11 |
| 100 | Arizona | La Paz | 04 | 12 |
| 101 | Arizona | Maricopa | 04 | 13 |
| 102 | Arizona | Mohave | 04 | 15 |
| 103 | Arizona | Navajo | 04 | 17 |
| 104 | Arizona | Pima | 04 | 19 |
| 105 | Arizona | Pinal | 04 | 21 |
| 106 | Arizona | Santa Cruz | 04 | 23 |
| 107 | Arizona | Yavapai | 04 | 25 |
| 108 | Arizona | Yuma | 04 | 27 |
| 109 | Arkansas | Arkansas | 05 | 1 |
| 110 | Arkansas | Ashley | 05 | 3 |
| 111 | Arkansas | Baxter | 05 | 5 |
| 112 | Arkansas | Benton | 05 | 7 |
| 113 | Arkansas | Boone | 05 | 9 |
| 114 | Arkansas | Bradley | 05 | 11 |
| 115 | Arkansas | Calhoun | 05 | 13 |
| 116 | Arkansas | Carroll | 05 | 15 |
| 117 | Arkansas | Chicot | 05 | 17 |
| 118 | Arkansas | Clark | 05 | 19 |
| 119 | Arkansas | Clay | 05 | 21 |
| 120 | Arkansas | Cleburne | 05 | 23 |
| 121 | Arkansas | Cleveland | 05 | 25 |
| 122 | Arkansas | Columbia | 05 | 27 |
| 123 | Arkansas | Conway | 05 | 29 |
| 124 | Arkansas | Craighead | 05 | 31 |
| 125 | Arkansas | Crawford | 05 | 33 |
| 126 | Arkansas | Crittenden | 05 | 35 |
| 127 | Arkansas | Cross | 05 | 37 |
| 128 | Arkansas | Dallas | 05 | 39 |
| 129 | Arkansas | Desha | 05 | 41 |
| 130 | Arkansas | Drew | 05 | 43 |
| 131 | Arkansas | Faulkner | 05 | 45 |
| 132 | Arkansas | Franklin | 05 | 47 |
| 133 | Arkansas | Fulton | 05 | 49 |
| 134 | Arkansas | Garland | 05 | 51 |
| 135 | Arkansas | Grant | 05 | 53 |
| 136 | Arkansas | Greene | 05 | 55 |
| 137 | Arkansas | Hempstead | 05 | 57 |
| 138 | Arkansas | Hot Spring | 05 | 59 |
| 139 | Arkansas | Howard | 05 | 61 |
| 140 | Arkansas | Independence | 05 | 63 |
| 141 | Arkansas | Izard | 05 | 65 |
| 142 | Arkansas | Jackson | 05 | 67 |
| 143 | Arkansas | Jefferson | 05 | 69 |
| 144 | Arkansas | Johnson | 05 | 71 |
| 145 | Arkansas | Lafayette | 05 | 73 |
| 146 | Arkansas | Lawrence | 05 | 75 |
| 147 | Arkansas | Lee | 05 | 77 |
| 148 | Arkansas | Lincoln | 05 | 79 |
| 149 | Arkansas | Little River | 05 | 81 |
| 150 | Arkansas | Logan | 05 | 83 |
| 151 | Arkansas | Lonoke | 05 | 85 |
| 152 | Arkansas | Madison | 05 | 87 |
| 153 | Arkansas | Marion | 05 | 89 |
| 154 | Arkansas | Miller | 05 | 91 |
| 155 | Arkansas | Mississippi | 05 | 93 |
| 156 | Arkansas | Monroe | 05 | 95 |
| 157 | Arkansas | Nevada | 05 | 99 |
| 158 | Arkansas | Newton | 05 | 101 |
| 159 | Arkansas | Ouachita | 05 | 103 |
| 160 | Arkansas | Perry | 05 | 105 |
| 161 | Arkansas | Phillips | 05 | 107 |
| 162 | Arkansas | Pike | 05 | 109 |
| 163 | Arkansas | Poinsett | 05 | 111 |
| 164 | Arkansas | Polk | 05 | 113 |
| 165 | Arkansas | Pope | 05 | 115 |
| 166 | Arkansas | Prairie | 05 | 117 |
| 167 | Arkansas | Pulaski | 05 | 119 |
| 168 | Arkansas | Randolph | 05 | 121 |
| 169 | Arkansas | St Francis | 05 | 123 |
| 170 | Arkansas | Saline | 05 | 125 |
| 171 | Arkansas | Scott | 05 | 127 |
| 172 | Arkansas | Searcy | 05 | 129 |
| 173 | Arkansas | Sebastian | 05 | 131 |
| 174 | Arkansas | Sevier | 05 | 133 |
| 175 | Arkansas | Sharp | 05 | 135 |
| 176 | Arkansas | Stone | 05 | 137 |
| 177 | Arkansas | Union | 05 | 139 |
| 178 | Arkansas | Van Buren | 05 | 141 |
| 179 | Arkansas | Washington | 05 | 143 |
| 180 | Arkansas | White | 05 | 145 |
| 181 | Arkansas | Woodruff | 05 | 147 |
| 182 | Arkansas | Yell | 05 | 149 |
| 183 | California | Alameda | 06 | 1 |
| 184 | California | Alpine | 06 | 3 |
| 185 | California | Amador | 06 | 5 |
| 186 | California | Butte | 06 | 7 |
| 187 | California | Calaveras | 06 | 9 |
| 188 | California | Colusa | 06 | 11 |
| 189 | California | Contra Costa | 06 | 13 |
| 190 | California | Del Norte | 06 | 15 |
| 191 | California | El Dorado | 06 | 17 |
| 192 | California | Fresno | 06 | 19 |
| 193 | California | Glenn | 06 | 21 |
| 194 | California | Humboldt | 06 | 23 |
| 195 | California | Imperial | 06 | 25 |
| 196 | California | Inyo | 06 | 27 |
| 197 | California | Kern | 06 | 29 |
| 198 | California | Kings | 06 | 31 |
| 199 | California | Lake | 06 | 33 |
| 200 | California | Lassen | 06 | 35 |
| 201 | California | Los Angeles | 06 | 37 |
| 202 | California | Madera | 06 | 39 |
| 203 | California | Marin | 06 | 41 |
| 204 | California | Mariposa | 06 | 43 |
| 205 | California | Mendocino | 06 | 45 |
| 206 | California | Merced | 06 | 47 |
| 207 | California | Modoc | 06 | 49 |
| 208 | California | Mono | 06 | 51 |
| 209 | California | Monterey | 06 | 53 |
| 210 | California | Napa | 06 | 55 |
| 211 | California | Nevada | 06 | 57 |
| 212 | California | Orange | 06 | 59 |
| 213 | California | Placer | 06 | 61 |
| 214 | California | Plumas | 06 | 63 |
| 215 | California | Riverside | 06 | 65 |
| 216 | California | Sacramento | 06 | 67 |
| 217 | California | San Benito | 06 | 69 |
| 218 | California | San Bernardino | 06 | 71 |
| 219 | California | San Diego | 06 | 73 |
| 220 | California | San Francisco | 06 | 75 |
| 221 | California | San Joaquin | 06 | 77 |
| 222 | California | San Luis Obispo | 06 | 79 |
| 223 | California | San Mateo | 06 | 81 |
| 224 | California | Santa Barbara | 06 | 83 |
| 225 | California | Santa Clara | 06 | 85 |
| 226 | California | Santa Cruz | 06 | 87 |
| 227 | California | Shasta | 06 | 89 |
| 228 | California | Sierra | 06 | 91 |
| 229 | California | Siskiyou | 06 | 93 |
| 230 | California | Solano | 06 | 95 |
| 231 | California | Sonoma | 06 | 97 |
| 232 | California | Stanislaus | 06 | 99 |
| 233 | California | Sutter | 06 | 101 |
| 234 | California | Tehama | 06 | 103 |
| 235 | California | Trinity | 06 | 105 |
| 236 | California | Tulare | 06 | 107 |
| 237 | California | Tuolumne | 06 | 109 |
| 238 | California | Ventura | 06 | 111 |
| 239 | California | Yolo | 06 | 113 |
| 240 | California | Yuba | 06 | 115 |
| 241 | Colorado | Adams | 08 | 1 |
| 242 | Colorado | Alamosa | 08 | 3 |
| 243 | Colorado | Arapahoe | 08 | 5 |
| 244 | Colorado | Archuleta | 08 | 7 |
| 245 | Colorado | Baca | 08 | 9 |
| 246 | Colorado | Bent | 08 | 11 |
| 247 | Colorado | Boulder | 08 | 13 |
| 248 | Colorado | Broomfield | 08 | 14 |
| 249 | Colorado | Chaffee | 08 | 15 |
| 250 | Colorado | Cheyenne | 08 | 17 |
| 251 | Colorado | Clear Creek | 08 | 19 |
| 252 | Colorado | Conejos | 08 | 21 |
| 253 | Colorado | Costilla | 08 | 23 |
| 254 | Colorado | Crowley | 08 | 25 |
| 255 | Colorado | Custer | 08 | 27 |
| 256 | Colorado | Delta | 08 | 29 |
| 257 | Colorado | Denver | 08 | 31 |
| 258 | Colorado | Dolores | 08 | 33 |
| 259 | Colorado | Douglas | 08 | 35 |
| 260 | Colorado | Eagle | 08 | 37 |
| 261 | Colorado | Elbert | 08 | 39 |
| 262 | Colorado | El Paso | 08 | 41 |
| 263 | Colorado | Fremont | 08 | 43 |
| 264 | Colorado | Garfield | 08 | 45 |
| 265 | Colorado | Gilpin | 08 | 47 |
| 266 | Colorado | Grand | 08 | 49 |
| 267 | Colorado | Gunnison | 08 | 51 |
| 268 | Colorado | Hinsdale | 08 | 53 |
| 269 | Colorado | Huerfano | 08 | 55 |
| 270 | Colorado | Jackson | 08 | 57 |
| 271 | Colorado | Jefferson | 08 | 59 |
| 272 | Colorado | Kiowa | 08 | 61 |
| 273 | Colorado | Kit Carson | 08 | 63 |
| 274 | Colorado | Lake | 08 | 65 |
| 275 | Colorado | La Plata | 08 | 67 |
| 276 | Colorado | Larimer | 08 | 69 |
| 277 | Colorado | Las Animas | 08 | 71 |
| 278 | Colorado | Lincoln | 08 | 73 |
| 279 | Colorado | Logan | 08 | 75 |
| 280 | Colorado | Mesa | 08 | 77 |
| 281 | Colorado | Mineral | 08 | 79 |
| 282 | Colorado | Moffat | 08 | 81 |
| 283 | Colorado | Montezuma | 08 | 83 |
| 284 | Colorado | Montrose | 08 | 85 |
| 285 | Colorado | Morgan | 08 | 87 |
| 286 | Colorado | Otero | 08 | 89 |
| 287 | Colorado | Ouray | 08 | 91 |
| 288 | Colorado | Park | 08 | 93 |
| 289 | Colorado | Phillips | 08 | 95 |
| 290 | Colorado | Pitkin | 08 | 97 |
| 291 | Colorado | Prowers | 08 | 99 |
| 292 | Colorado | Pueblo | 08 | 101 |
| 293 | Colorado | Rio Blanco | 08 | 103 |
| 294 | Colorado | Rio Grande | 08 | 105 |
| 295 | Colorado | Routt | 08 | 107 |
| 296 | Colorado | Saguache | 08 | 109 |
| 297 | Colorado | San Juan | 08 | 111 |
| 298 | Colorado | San Miguel | 08 | 113 |
| 299 | Colorado | Sedgwick | 08 | 115 |
| 300 | Colorado | Summit | 08 | 117 |
| 301 | Colorado | Teller | 08 | 119 |
| 302 | Colorado | Washington | 08 | 121 |
| 303 | Colorado | Weld | 08 | 123 |
| 304 | Colorado | Yuma | 08 | 125 |
| 305 | Connecticut | Fairfield | 09 | 1 |
| 306 | Connecticut | Hartford | 09 | 3 |
| 307 | Connecticut | Litchfield | 09 | 5 |
| 308 | Connecticut | Middlesex | 09 | 7 |
| 309 | Connecticut | New Haven | 09 | 9 |
| 310 | Connecticut | New London | 09 | 11 |
| 311 | Connecticut | Tolland | 09 | 13 |
| 312 | Connecticut | Windham | 09 | 15 |
| 313 | Delaware | Kent | 10 | 1 |
| 314 | Delaware | New Castle | 10 | 3 |
| 315 | Delaware | Sussex | 10 | 5 |
| 316 | District of Columbia | District of Columbia | 11 | 1 |
| 317 | District of Columbia | Montgomery | 11 | 31 |
| 318 | Florida | Alachua | 12 | 1 |
| 319 | Florida | Baker | 12 | 3 |
| 320 | Florida | Bay | 12 | 5 |
| 321 | Florida | Bradford | 12 | 7 |
| 322 | Florida | Brevard | 12 | 9 |
| 323 | Florida | Broward | 12 | 11 |
| 324 | Florida | Calhoun | 12 | 13 |
| 325 | Florida | Charlotte | 12 | 15 |
| 326 | Florida | Citrus | 12 | 17 |
| 327 | Florida | Clay | 12 | 19 |
| 328 | Florida | Collier | 12 | 21 |
| 329 | Florida | Columbia | 12 | 23 |
| 330 | Florida | De Soto | 12 | 27 |
| 331 | Florida | Dixie | 12 | 29 |
| 332 | Florida | Duval | 12 | 31 |
| 333 | Florida | Escambia | 12 | 33 |
| 334 | Florida | Flagler | 12 | 35 |
| 335 | Florida | Franklin | 12 | 37 |
| 336 | Florida | Gadsden | 12 | 39 |
| 337 | Florida | Gilchrist | 12 | 41 |
| 338 | Florida | Glades | 12 | 43 |
| 339 | Florida | Gulf | 12 | 45 |
| 340 | Florida | Hamilton | 12 | 47 |
| 341 | Florida | Hardee | 12 | 49 |
| 342 | Florida | Hendry | 12 | 51 |
| 343 | Florida | Hernando | 12 | 53 |
| 344 | Florida | Highlands | 12 | 55 |
| 345 | Florida | Hillsborough | 12 | 57 |
| 346 | Florida | Holmes | 12 | 59 |
| 347 | Florida | Indian River | 12 | 61 |
| 348 | Florida | Jackson | 12 | 63 |
| 349 | Florida | Jefferson | 12 | 65 |
| 350 | Florida | Lafayette | 12 | 67 |
| 351 | Florida | Lake | 12 | 69 |
| 352 | Florida | Lee | 12 | 71 |
| 353 | Florida | Leon | 12 | 73 |
| 354 | Florida | Levy | 12 | 75 |
| 355 | Florida | Liberty | 12 | 77 |
| 356 | Florida | Madison | 12 | 79 |
| 357 | Florida | Manatee | 12 | 81 |
| 358 | Florida | Marion | 12 | 83 |
| 359 | Florida | Martin | 12 | 85 |
| 360 | Florida | Miami-Dade | 12 | 86 |
| 361 | Florida | Monroe | 12 | 87 |
| 362 | Florida | Nassau | 12 | 89 |
| 363 | Florida | Okaloosa | 12 | 91 |
| 364 | Florida | Okeechobee | 12 | 93 |
| 365 | Florida | Orange | 12 | 95 |
| 366 | Florida | Osceola | 12 | 97 |
| 367 | Florida | Palm Beach | 12 | 99 |
| 368 | Florida | Pasco | 12 | 101 |
| 369 | Florida | Pinellas | 12 | 103 |
| 370 | Florida | Polk | 12 | 105 |
| 371 | Florida | Putnam | 12 | 107 |
| 372 | Florida | St Johns | 12 | 109 |
| 373 | Florida | St Lucie | 12 | 111 |
| 374 | Florida | Santa Rosa | 12 | 113 |
| 375 | Florida | Sarasota | 12 | 115 |
| 376 | Florida | Seminole | 12 | 117 |
| 377 | Florida | Sumter | 12 | 119 |
| 378 | Florida | Suwannee | 12 | 121 |
| 379 | Florida | Taylor | 12 | 123 |
| 380 | Florida | Union | 12 | 125 |
| 381 | Florida | Volusia | 12 | 127 |
| 382 | Florida | Wakulla | 12 | 129 |
| 383 | Florida | Walton | 12 | 131 |
| 384 | Florida | Washington | 12 | 133 |
| 385 | Georgia | Appling | 13 | 1 |
| 386 | Georgia | Atkinson | 13 | 3 |
| 387 | Georgia | Bacon | 13 | 5 |
| 388 | Georgia | Baker | 13 | 7 |
| 389 | Georgia | Baldwin | 13 | 9 |
| 390 | Georgia | Banks | 13 | 11 |
| 391 | Georgia | Barrow | 13 | 13 |
| 392 | Georgia | Bartow | 13 | 15 |
| 393 | Georgia | Ben Hill | 13 | 17 |
| 394 | Georgia | Berrien | 13 | 19 |
| 395 | Georgia | Bibb | 13 | 21 |
| 396 | Georgia | Bleckley | 13 | 23 |
| 397 | Georgia | Brantley | 13 | 25 |
| 398 | Georgia | Brooks | 13 | 27 |
| 399 | Georgia | Bryan | 13 | 29 |
| 400 | Georgia | Bulloch | 13 | 31 |
| 401 | Georgia | Burke | 13 | 33 |
| 402 | Georgia | Butts | 13 | 35 |
| 403 | Georgia | Calhoun | 13 | 37 |
| 404 | Georgia | Camden | 13 | 39 |
| 405 | Georgia | Candler | 13 | 43 |
| 406 | Georgia | Carroll | 13 | 45 |
| 407 | Georgia | Catoosa | 13 | 47 |
| 408 | Georgia | Charlton | 13 | 49 |
| 409 | Georgia | Chatham | 13 | 51 |
| ... | ... | ... | ... | ... |
| 2732 | Texas | Somervell | 48 | 425 |
| 2733 | Texas | Starr | 48 | 427 |
| 2734 | Texas | Stephens | 48 | 429 |
| 2735 | Texas | Sterling | 48 | 431 |
| 2736 | Texas | Stonewall | 48 | 433 |
| 2737 | Texas | Sutton | 48 | 435 |
| 2738 | Texas | Swisher | 48 | 437 |
| 2739 | Texas | Tarrant | 48 | 439 |
| 2740 | Texas | Taylor | 48 | 441 |
| 2741 | Texas | Terrell | 48 | 443 |
| 2742 | Texas | Terry | 48 | 445 |
| 2743 | Texas | Throckmorton | 48 | 447 |
| 2744 | Texas | Titus | 48 | 449 |
| 2745 | Texas | Tom Green | 48 | 451 |
| 2746 | Texas | Travis | 48 | 453 |
| 2747 | Texas | Trinity | 48 | 455 |
| 2748 | Texas | Tyler | 48 | 457 |
| 2749 | Texas | Upshur | 48 | 459 |
| 2750 | Texas | Upton | 48 | 461 |
| 2751 | Texas | Uvalde | 48 | 463 |
| 2752 | Texas | Val Verde | 48 | 465 |
| 2753 | Texas | Van Zandt | 48 | 467 |
| 2754 | Texas | Victoria | 48 | 469 |
| 2755 | Texas | Walker | 48 | 471 |
| 2756 | Texas | Waller | 48 | 473 |
| 2757 | Texas | Ward | 48 | 475 |
| 2758 | Texas | Washington | 48 | 477 |
| 2759 | Texas | Webb | 48 | 479 |
| 2760 | Texas | Wharton | 48 | 481 |
| 2761 | Texas | Wheeler | 48 | 483 |
| 2762 | Texas | Wichita | 48 | 485 |
| 2763 | Texas | Wilbarger | 48 | 487 |
| 2764 | Texas | Willacy | 48 | 489 |
| 2765 | Texas | Williamson | 48 | 491 |
| 2766 | Texas | Wilson | 48 | 493 |
| 2767 | Texas | Winkler | 48 | 495 |
| 2768 | Texas | Wise | 48 | 497 |
| 2769 | Texas | Wood | 48 | 499 |
| 2770 | Texas | Yoakum | 48 | 501 |
| 2771 | Texas | Young | 48 | 503 |
| 2772 | Texas | Zapata | 48 | 505 |
| 2773 | Texas | Zavala | 48 | 507 |
| 2774 | Utah | Beaver | 49 | 1 |
| 2775 | Utah | Box Elder | 49 | 3 |
| 2776 | Utah | Cache | 49 | 5 |
| 2777 | Utah | Carbon | 49 | 7 |
| 2778 | Utah | Daggett | 49 | 9 |
| 2779 | Utah | Davis | 49 | 11 |
| 2780 | Utah | Duchesne | 49 | 13 |
| 2781 | Utah | Emery | 49 | 15 |
| 2782 | Utah | Garfield | 49 | 17 |
| 2783 | Utah | Grand | 49 | 19 |
| 2784 | Utah | Iron | 49 | 21 |
| 2785 | Utah | Juab | 49 | 23 |
| 2786 | Utah | Kane | 49 | 25 |
| 2787 | Utah | Millard | 49 | 27 |
| 2788 | Utah | Morgan | 49 | 29 |
| 2789 | Utah | Piute | 49 | 31 |
| 2790 | Utah | Rich | 49 | 33 |
| 2791 | Utah | Salt Lake | 49 | 35 |
| 2792 | Utah | San Juan | 49 | 37 |
| 2793 | Utah | Sanpete | 49 | 39 |
| 2794 | Utah | Sevier | 49 | 41 |
| 2795 | Utah | Summit | 49 | 43 |
| 2796 | Utah | Tooele | 49 | 45 |
| 2797 | Utah | Uintah | 49 | 47 |
| 2798 | Utah | Utah | 49 | 49 |
| 2799 | Utah | Wasatch | 49 | 51 |
| 2800 | Utah | Washington | 49 | 53 |
| 2801 | Utah | Wayne | 49 | 55 |
| 2802 | Utah | Weber | 49 | 57 |
| 2803 | Vermont | Addison | 50 | 1 |
| 2804 | Vermont | Bennington | 50 | 3 |
| 2805 | Vermont | Caledonia | 50 | 5 |
| 2806 | Vermont | Chittenden | 50 | 7 |
| 2807 | Vermont | Essex | 50 | 9 |
| 2808 | Vermont | Franklin | 50 | 11 |
| 2809 | Vermont | Grand Isle | 50 | 13 |
| 2810 | Vermont | Lamoille | 50 | 15 |
| 2811 | Vermont | Orange | 50 | 17 |
| 2812 | Vermont | Orleans | 50 | 19 |
| 2813 | Vermont | Rutland | 50 | 21 |
| 2814 | Vermont | Washington | 50 | 23 |
| 2815 | Vermont | Windham | 50 | 25 |
| 2816 | Vermont | Windsor | 50 | 27 |
| 2817 | Virginia | Accomack | 51 | 1 |
| 2818 | Virginia | Albemarle | 51 | 3 |
| 2819 | Virginia | Alleghany | 51 | 5 |
| 2820 | Virginia | Amelia | 51 | 7 |
| 2821 | Virginia | Amherst | 51 | 9 |
| 2822 | Virginia | Appomattox | 51 | 11 |
| 2823 | Virginia | Arlington | 51 | 13 |
| 2824 | Virginia | Augusta | 51 | 15 |
| 2825 | Virginia | Bath | 51 | 17 |
| 2826 | Virginia | Bedford | 51 | 19 |
| 2827 | Virginia | Bland | 51 | 21 |
| 2828 | Virginia | Botetourt | 51 | 23 |
| 2829 | Virginia | Brunswick | 51 | 25 |
| 2830 | Virginia | Buchanan | 51 | 27 |
| 2831 | Virginia | Buckingham | 51 | 29 |
| 2832 | Virginia | Campbell | 51 | 31 |
| 2833 | Virginia | Caroline | 51 | 33 |
| 2834 | Virginia | Carroll | 51 | 35 |
| 2835 | Virginia | Charles City | 51 | 36 |
| 2836 | Virginia | Charlotte | 51 | 37 |
| 2837 | Virginia | Chesterfield | 51 | 41 |
| 2838 | Virginia | Clarke | 51 | 43 |
| 2839 | Virginia | Craig | 51 | 45 |
| 2840 | Virginia | Culpeper | 51 | 47 |
| 2841 | Virginia | Cumberland | 51 | 49 |
| 2842 | Virginia | Dickenson | 51 | 51 |
| 2843 | Virginia | Dinwiddie | 51 | 53 |
| 2844 | Virginia | Essex | 51 | 57 |
| 2845 | Virginia | Fairfax | 51 | 59 |
| 2846 | Virginia | Fauquier | 51 | 61 |
| 2847 | Virginia | Floyd | 51 | 63 |
| 2848 | Virginia | Fluvanna | 51 | 65 |
| 2849 | Virginia | Franklin | 51 | 67 |
| 2850 | Virginia | Frederick | 51 | 69 |
| 2851 | Virginia | Giles | 51 | 71 |
| 2852 | Virginia | Gloucester | 51 | 73 |
| 2853 | Virginia | Goochland | 51 | 75 |
| 2854 | Virginia | Grayson | 51 | 77 |
| 2855 | Virginia | Greene | 51 | 79 |
| 2856 | Virginia | Greensville | 51 | 81 |
| 2857 | Virginia | Halifax | 51 | 83 |
| 2858 | Virginia | Hanover | 51 | 85 |
| 2859 | Virginia | Henrico | 51 | 87 |
| 2860 | Virginia | Henry | 51 | 89 |
| 2861 | Virginia | Highland | 51 | 91 |
| 2862 | Virginia | Isle of Wight | 51 | 93 |
| 2863 | Virginia | James City | 51 | 95 |
| 2864 | Virginia | King and Queen | 51 | 97 |
| 2865 | Virginia | King George | 51 | 99 |
| 2866 | Virginia | King William | 51 | 101 |
| 2867 | Virginia | Lancaster | 51 | 103 |
| 2868 | Virginia | Lee | 51 | 105 |
| 2869 | Virginia | Loudoun | 51 | 107 |
| 2870 | Virginia | Louisa | 51 | 109 |
| 2871 | Virginia | Lunenburg | 51 | 111 |
| 2872 | Virginia | Madison | 51 | 113 |
| 2873 | Virginia | Mathews | 51 | 115 |
| 2874 | Virginia | Mecklenburg | 51 | 117 |
| 2875 | Virginia | Middlesex | 51 | 119 |
| 2876 | Virginia | Montgomery | 51 | 121 |
| 2877 | Virginia | Nelson | 51 | 125 |
| 2878 | Virginia | New Kent | 51 | 127 |
| 2879 | Virginia | Northampton | 51 | 131 |
| 2880 | Virginia | Northumberland | 51 | 133 |
| 2881 | Virginia | Nottoway | 51 | 135 |
| 2882 | Virginia | Orange | 51 | 137 |
| 2883 | Virginia | Page | 51 | 139 |
| 2884 | Virginia | Patrick | 51 | 141 |
| 2885 | Virginia | Pittsylvania | 51 | 143 |
| 2886 | Virginia | Powhatan | 51 | 145 |
| 2887 | Virginia | Prince Edward | 51 | 147 |
| 2888 | Virginia | Prince George | 51 | 149 |
| 2889 | Virginia | Prince William | 51 | 153 |
| 2890 | Virginia | Pulaski | 51 | 155 |
| 2891 | Virginia | Rappahannock | 51 | 157 |
| 2892 | Virginia | Richmond | 51 | 159 |
| 2893 | Virginia | Roanoke | 51 | 161 |
| 2894 | Virginia | Rockbridge | 51 | 163 |
| 2895 | Virginia | Rockingham | 51 | 165 |
| 2896 | Virginia | Russell | 51 | 167 |
| 2897 | Virginia | Scott | 51 | 169 |
| 2898 | Virginia | Shenandoah | 51 | 171 |
| 2899 | Virginia | Smyth | 51 | 173 |
| 2900 | Virginia | Southampton | 51 | 175 |
| 2901 | Virginia | Spotsylvania | 51 | 177 |
| 2902 | Virginia | Stafford | 51 | 179 |
| 2903 | Virginia | Surry | 51 | 181 |
| 2904 | Virginia | Sussex | 51 | 183 |
| 2905 | Virginia | Tazewell | 51 | 185 |
| 2906 | Virginia | Warren | 51 | 187 |
| 2907 | Virginia | Washington | 51 | 191 |
| 2908 | Virginia | Westmoreland | 51 | 193 |
| 2909 | Virginia | Wise | 51 | 195 |
| 2910 | Virginia | Wythe | 51 | 197 |
| 2911 | Virginia | York | 51 | 199 |
| 2912 | Virginia | Alexandria City | 51 | 510 |
| 2913 | Virginia | Bedford City | 51 | 515 |
| 2914 | Virginia | Bristol City | 51 | 520 |
| 2915 | Virginia | Buena Vista City | 51 | 530 |
| 2916 | Virginia | Charlottesville City | 51 | 540 |
| 2917 | Virginia | Chesapeake City | 51 | 550 |
| 2918 | Virginia | Clifton Forge City | 51 | 560 |
| 2919 | Virginia | Colonial Heights City | 51 | 570 |
| 2920 | Virginia | Covington City | 51 | 580 |
| 2921 | Virginia | Danville City | 51 | 590 |
| 2922 | Virginia | Emporia City | 51 | 595 |
| 2923 | Virginia | Fairfax City | 51 | 600 |
| 2924 | Virginia | Falls Church City | 51 | 610 |
| 2925 | Virginia | Franklin City | 51 | 620 |
| 2926 | Virginia | Fredericksburg City | 51 | 630 |
| 2927 | Virginia | Galax City | 51 | 640 |
| 2928 | Virginia | Hampton City | 51 | 650 |
| 2929 | Virginia | Harrisonburg City | 51 | 660 |
| 2930 | Virginia | Hopewell City | 51 | 670 |
| 2931 | Virginia | Lexington City | 51 | 678 |
| 2932 | Virginia | Lynchburg City | 51 | 680 |
| 2933 | Virginia | Manassas City | 51 | 683 |
| 2934 | Virginia | Manassas Park City | 51 | 685 |
| 2935 | Virginia | Martinsville City | 51 | 690 |
| 2936 | Virginia | Newport News City | 51 | 700 |
| 2937 | Virginia | Norfolk City | 51 | 710 |
| 2938 | Virginia | Norton City | 51 | 720 |
| 2939 | Virginia | Petersburg City | 51 | 730 |
| 2940 | Virginia | Poquoson City | 51 | 735 |
| 2941 | Virginia | Portsmouth City | 51 | 740 |
| 2942 | Virginia | Radford | 51 | 750 |
| 2943 | Virginia | Richmond City | 51 | 760 |
| 2944 | Virginia | Roanoke City | 51 | 770 |
| 2945 | Virginia | Salem City | 51 | 775 |
| 2946 | Virginia | South Boston City | 51 | 780 |
| 2947 | Virginia | Staunton City | 51 | 790 |
| 2948 | Virginia | Suffolk City | 51 | 800 |
| 2949 | Virginia | Virginia Beach City | 51 | 810 |
| 2950 | Virginia | Waynesboro City | 51 | 820 |
| 2951 | Virginia | Williamsburg City | 51 | 830 |
| 2952 | Virginia | Winchester City | 51 | 840 |
| 2953 | Washington | Adams | 53 | 1 |
| 2954 | Washington | Asotin | 53 | 3 |
| 2955 | Washington | Benton | 53 | 5 |
| 2956 | Washington | Chelan | 53 | 7 |
| 2957 | Washington | Clallam | 53 | 9 |
| 2958 | Washington | Clark | 53 | 11 |
| 2959 | Washington | Columbia | 53 | 13 |
| 2960 | Washington | Cowlitz | 53 | 15 |
| 2961 | Washington | Douglas | 53 | 17 |
| 2962 | Washington | Ferry | 53 | 19 |
| 2963 | Washington | Franklin | 53 | 21 |
| 2964 | Washington | Garfield | 53 | 23 |
| 2965 | Washington | Grant | 53 | 25 |
| 2966 | Washington | Grays Harbor | 53 | 27 |
| 2967 | Washington | Island | 53 | 29 |
| 2968 | Washington | Jefferson | 53 | 31 |
| 2969 | Washington | King | 53 | 33 |
| 2970 | Washington | Kitsap | 53 | 35 |
| 2971 | Washington | Kittitas | 53 | 37 |
| 2972 | Washington | Klickitat | 53 | 39 |
| 2973 | Washington | Lewis | 53 | 41 |
| 2974 | Washington | Lincoln | 53 | 43 |
| 2975 | Washington | Mason | 53 | 45 |
| 2976 | Washington | Okanogan | 53 | 47 |
| 2977 | Washington | Pacific | 53 | 49 |
| 2978 | Washington | Pend Oreille | 53 | 51 |
| 2979 | Washington | Pierce | 53 | 53 |
| 2980 | Washington | San Juan | 53 | 55 |
| 2981 | Washington | Skagit | 53 | 57 |
| 2982 | Washington | Skamania | 53 | 59 |
| 2983 | Washington | Snohomish | 53 | 61 |
| 2984 | Washington | Spokane | 53 | 63 |
| 2985 | Washington | Stevens | 53 | 65 |
| 2986 | Washington | Thurston | 53 | 67 |
| 2987 | Washington | Wahkiakum | 53 | 69 |
| 2988 | Washington | Walla Walla | 53 | 71 |
| 2989 | Washington | Whatcom | 53 | 73 |
| 2990 | Washington | Whitman | 53 | 75 |
| 2991 | Washington | Yakima | 53 | 77 |
| 2992 | West Virginia | Barbour | 54 | 1 |
| 2993 | West Virginia | Berkeley | 54 | 3 |
| 2994 | West Virginia | Boone | 54 | 5 |
| 2995 | West Virginia | Braxton | 54 | 7 |
| 2996 | West Virginia | Brooke | 54 | 9 |
| 2997 | West Virginia | Cabell | 54 | 11 |
| 2998 | West Virginia | Calhoun | 54 | 13 |
| 2999 | West Virginia | Clay | 54 | 15 |
| 3000 | West Virginia | Doddridge | 54 | 17 |
| 3001 | West Virginia | Fayette | 54 | 19 |
| 3002 | West Virginia | Gilmer | 54 | 21 |
| 3003 | West Virginia | Grant | 54 | 23 |
| 3004 | West Virginia | Greenbrier | 54 | 25 |
| 3005 | West Virginia | Hampshire | 54 | 27 |
| 3006 | West Virginia | Hancock | 54 | 29 |
| 3007 | West Virginia | Hardy | 54 | 31 |
| 3008 | West Virginia | Harrison | 54 | 33 |
| 3009 | West Virginia | Jackson | 54 | 35 |
| 3010 | West Virginia | Jefferson | 54 | 37 |
| 3011 | West Virginia | Kanawha | 54 | 39 |
| 3012 | West Virginia | Lewis | 54 | 41 |
| 3013 | West Virginia | Lincoln | 54 | 43 |
| 3014 | West Virginia | Logan | 54 | 45 |
| 3015 | West Virginia | McDowell | 54 | 47 |
| 3016 | West Virginia | Marion | 54 | 49 |
| 3017 | West Virginia | Marshall | 54 | 51 |
| 3018 | West Virginia | Mason | 54 | 53 |
| 3019 | West Virginia | Mercer | 54 | 55 |
| 3020 | West Virginia | Mineral | 54 | 57 |
| 3021 | West Virginia | Mingo | 54 | 59 |
| 3022 | West Virginia | Monongalia | 54 | 61 |
| 3023 | West Virginia | Monroe | 54 | 63 |
| 3024 | West Virginia | Morgan | 54 | 65 |
| 3025 | West Virginia | Nicholas | 54 | 67 |
| 3026 | West Virginia | Ohio | 54 | 69 |
| 3027 | West Virginia | Pendleton | 54 | 71 |
| 3028 | West Virginia | Pleasants | 54 | 73 |
| 3029 | West Virginia | Pocahontas | 54 | 75 |
| 3030 | West Virginia | Preston | 54 | 77 |
| 3031 | West Virginia | Putnam | 54 | 79 |
| 3032 | West Virginia | Raleigh | 54 | 81 |
| 3033 | West Virginia | Randolph | 54 | 83 |
| 3034 | West Virginia | Ritchie | 54 | 85 |
| 3035 | West Virginia | Roane | 54 | 87 |
| 3036 | West Virginia | Summers | 54 | 89 |
| 3037 | West Virginia | Taylor | 54 | 91 |
| 3038 | West Virginia | Tucker | 54 | 93 |
| 3039 | West Virginia | Tyler | 54 | 95 |
| 3040 | West Virginia | Upshur | 54 | 97 |
| 3041 | West Virginia | Wayne | 54 | 99 |
| 3042 | West Virginia | Webster | 54 | 101 |
| 3043 | West Virginia | Wetzel | 54 | 103 |
| 3044 | West Virginia | Wirt | 54 | 105 |
| 3045 | West Virginia | Wood | 54 | 107 |
| 3046 | West Virginia | Wyoming | 54 | 109 |
| 3047 | Wisconsin | Adams | 55 | 1 |
| 3048 | Wisconsin | Ashland | 55 | 3 |
| 3049 | Wisconsin | Barron | 55 | 5 |
| 3050 | Wisconsin | Bayfield | 55 | 7 |
| 3051 | Wisconsin | Brown | 55 | 9 |
| 3052 | Wisconsin | Buffalo | 55 | 11 |
| 3053 | Wisconsin | Burnett | 55 | 13 |
| 3054 | Wisconsin | Calumet | 55 | 15 |
| 3055 | Wisconsin | Chippewa | 55 | 17 |
| 3056 | Wisconsin | Clark | 55 | 19 |
| 3057 | Wisconsin | Columbia | 55 | 21 |
| 3058 | Wisconsin | Crawford | 55 | 23 |
| 3059 | Wisconsin | Dane | 55 | 25 |
| 3060 | Wisconsin | Dodge | 55 | 27 |
| 3061 | Wisconsin | Door | 55 | 29 |
| 3062 | Wisconsin | Douglas | 55 | 31 |
| 3063 | Wisconsin | Dunn | 55 | 33 |
| 3064 | Wisconsin | Eau Claire | 55 | 35 |
| 3065 | Wisconsin | Florence | 55 | 37 |
| 3066 | Wisconsin | Fond Du Lac | 55 | 39 |
| 3067 | Wisconsin | Forest | 55 | 41 |
| 3068 | Wisconsin | Grant | 55 | 43 |
| 3069 | Wisconsin | Green | 55 | 45 |
| 3070 | Wisconsin | Green Lake | 55 | 47 |
| 3071 | Wisconsin | Iowa | 55 | 49 |
| 3072 | Wisconsin | Iron | 55 | 51 |
| 3073 | Wisconsin | Jackson | 55 | 53 |
| 3074 | Wisconsin | Jefferson | 55 | 55 |
| 3075 | Wisconsin | Juneau | 55 | 57 |
| 3076 | Wisconsin | Kenosha | 55 | 59 |
| 3077 | Wisconsin | Kewaunee | 55 | 61 |
| 3078 | Wisconsin | La Crosse | 55 | 63 |
| 3079 | Wisconsin | Lafayette | 55 | 65 |
| 3080 | Wisconsin | Langlade | 55 | 67 |
| 3081 | Wisconsin | Lincoln | 55 | 69 |
| 3082 | Wisconsin | Manitowoc | 55 | 71 |
| 3083 | Wisconsin | Marathon | 55 | 73 |
| 3084 | Wisconsin | Marinette | 55 | 75 |
| 3085 | Wisconsin | Marquette | 55 | 77 |
| 3086 | Wisconsin | Menominee | 55 | 78 |
| 3087 | Wisconsin | Milwaukee | 55 | 79 |
| 3088 | Wisconsin | Monroe | 55 | 81 |
| 3089 | Wisconsin | Oconto | 55 | 83 |
| 3090 | Wisconsin | Oneida | 55 | 85 |
| 3091 | Wisconsin | Outagamie | 55 | 87 |
| 3092 | Wisconsin | Ozaukee | 55 | 89 |
| 3093 | Wisconsin | Pepin | 55 | 91 |
| 3094 | Wisconsin | Pierce | 55 | 93 |
| 3095 | Wisconsin | Polk | 55 | 95 |
| 3096 | Wisconsin | Portage | 55 | 97 |
| 3097 | Wisconsin | Price | 55 | 99 |
| 3098 | Wisconsin | Racine | 55 | 101 |
| 3099 | Wisconsin | Richland | 55 | 103 |
| 3100 | Wisconsin | Rock | 55 | 105 |
| 3101 | Wisconsin | Rusk | 55 | 107 |
| 3102 | Wisconsin | St Croix | 55 | 109 |
| 3103 | Wisconsin | Sauk | 55 | 111 |
| 3104 | Wisconsin | Sawyer | 55 | 113 |
| 3105 | Wisconsin | Shawano | 55 | 115 |
| 3106 | Wisconsin | Sheboygan | 55 | 117 |
| 3107 | Wisconsin | Taylor | 55 | 119 |
| 3108 | Wisconsin | Trempealeau | 55 | 121 |
| 3109 | Wisconsin | Vernon | 55 | 123 |
| 3110 | Wisconsin | Vilas | 55 | 125 |
| 3111 | Wisconsin | Walworth | 55 | 127 |
| 3112 | Wisconsin | Washburn | 55 | 129 |
| 3113 | Wisconsin | Washington | 55 | 131 |
| 3114 | Wisconsin | Waukesha | 55 | 133 |
| 3115 | Wisconsin | Waupaca | 55 | 135 |
| 3116 | Wisconsin | Waushara | 55 | 137 |
| 3117 | Wisconsin | Winnebago | 55 | 139 |
| 3118 | Wisconsin | Wood | 55 | 141 |
| 3119 | Wyoming | Albany | 56 | 1 |
| 3120 | Wyoming | Big Horn | 56 | 3 |
| 3121 | Wyoming | Campbell | 56 | 5 |
| 3122 | Wyoming | Carbon | 56 | 7 |
| 3123 | Wyoming | Converse | 56 | 9 |
| 3124 | Wyoming | Crook | 56 | 11 |
| 3125 | Wyoming | Fremont | 56 | 13 |
| 3126 | Wyoming | Goshen | 56 | 15 |
| 3127 | Wyoming | Hot Springs | 56 | 17 |
| 3128 | Wyoming | Johnson | 56 | 19 |
| 3129 | Wyoming | Laramie | 56 | 21 |
| 3130 | Wyoming | Lincoln | 56 | 23 |
| 3131 | Wyoming | Natrona | 56 | 25 |
| 3132 | Wyoming | Niobrara | 56 | 27 |
| 3133 | Wyoming | Park | 56 | 29 |
| 3134 | Wyoming | Platte | 56 | 31 |
| 3135 | Wyoming | Sheridan | 56 | 33 |
| 3136 | Wyoming | Sublette | 56 | 35 |
| 3137 | Wyoming | Sweetwater | 56 | 37 |
| 3138 | Wyoming | Teton | 56 | 39 |
| 3139 | Wyoming | Uinta | 56 | 41 |
| 3140 | Wyoming | Washakie | 56 | 43 |
| 3141 | Wyoming | Weston | 56 | 45 |
3142 rows × 4 columns
df_1['FIPS County'] = df_1['FIPS County'].apply(lambda x: '{0:0>3}'.format(x))
df_1.head(5)
| State | County Name | FIPS State | FIPS County | |
|---|---|---|---|---|
| 0 | Alabama | Autauga | 01 | 001 |
| 1 | Alabama | Baldwin | 01 | 003 |
| 2 | Alabama | Barbour | 01 | 005 |
| 3 | Alabama | Bibb | 01 | 007 |
| 4 | Alabama | Blount | 01 | 009 |
df_1.columns = ['state', 'county_name', 'fips_state', 'fips_county']
df_1.head(5)
| state | county_name | fips_state | fips_county | |
|---|---|---|---|---|
| 0 | Alabama | Autauga | 01 | 001 |
| 1 | Alabama | Baldwin | 01 | 003 |
| 2 | Alabama | Barbour | 01 | 005 |
| 3 | Alabama | Bibb | 01 | 007 |
| 4 | Alabama | Blount | 01 | 009 |
df_1['FIPS'] = df_1["fips_state"] + df_1["fips_county"]
df_1.head(5)
| state | county_name | fips_state | fips_county | FIPS | |
|---|---|---|---|---|---|
| 0 | Alabama | Autauga | 01 | 001 | 01001 |
| 1 | Alabama | Baldwin | 01 | 003 | 01003 |
| 2 | Alabama | Barbour | 01 | 005 | 01005 |
| 3 | Alabama | Bibb | 01 | 007 | 01007 |
| 4 | Alabama | Blount | 01 | 009 | 01009 |
df_1[df_1.index.duplicated()]
| state | county_name | fips_state | fips_county | FIPS |
|---|
print('FIPS' in df_1)
True
print(df_1.dtypes)
state object county_name object fips_state object fips_county object FIPS object dtype: object
df_map = df[['county', 'state', 'GEOID','percent_no_internet']].copy()
def func(row):
return row.GEOID[-5:]
df_map['FIPS'] = df.apply(func,axis=1)
#pd.set_option('display.max_columns', 24)
df_map.head(5)
| county | state | GEOID | percent_no_internet | FIPS | |
|---|---|---|---|---|---|
| 0 | Anchorage Municipality | AK | 05000US02020 | 6.593887 | 02020 |
| 1 | Fairbanks North Star Borough | AK | 05000US02090 | 12.102458 | 02090 |
| 2 | Matanuska-Susitna Borough | AK | 05000US02170 | 11.156575 | 02170 |
| 3 | Baldwin County | AL | 05000US01003 | 17.868167 | 01003 |
| 4 | Calhoun County | AL | 05000US01015 | 23.464932 | 01015 |
df_map['percent_no_internet'] = df_map['percent_no_internet'].astype(float)
df_map.head(5)
| county | state | GEOID | percent_no_internet | FIPS | |
|---|---|---|---|---|---|
| 0 | Anchorage Municipality | AK | 05000US02020 | 6.593887 | 02020 |
| 1 | Fairbanks North Star Borough | AK | 05000US02090 | 12.102458 | 02090 |
| 2 | Matanuska-Susitna Borough | AK | 05000US02170 | 11.156575 | 02170 |
| 3 | Baldwin County | AL | 05000US01003 | 17.868167 | 01003 |
| 4 | Calhoun County | AL | 05000US01015 | 23.464932 | 01015 |
print(df_map.dtypes)
county object state object GEOID object percent_no_internet float64 FIPS object dtype: object
df_clean = pd.merge(df_map, df_1, on='FIPS', how='outer')
df_clean.head(5)
| county | state_x | GEOID | percent_no_internet | FIPS | state_y | county_name | fips_state | fips_county | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | Anchorage Municipality | AK | 05000US02020 | 6.593887 | 02020 | Alaska | Anchorage | 02 | 020 |
| 1 | Fairbanks North Star Borough | AK | 05000US02090 | 12.102458 | 02090 | Alaska | Fairbanks North Star | 02 | 090 |
| 2 | Matanuska-Susitna Borough | AK | 05000US02170 | 11.156575 | 02170 | Alaska | Matanuska Susitna | 02 | 170 |
| 3 | Baldwin County | AL | 05000US01003 | 17.868167 | 01003 | Alabama | Baldwin | 01 | 003 |
| 4 | Calhoun County | AL | 05000US01015 | 23.464932 | 01015 | Alabama | Calhoun | 01 | 015 |
df_clean["percent_no_internet"].fillna(0, inplace=True)
#county chloropleth map of households without internet
colorscale = ["#b8babc","#ebf3fb","#deebf7","#d2e3f3","#c6dbef","#b3d2e9","#9ecae1",
"#85bcdb","#6baed6","#57a0ce","#4292c6","#3082be","#2171b5","#1361a9",
"#08519c","#0b4083","#08306b"]
endpts = list(np.linspace(0, 55, len(colorscale) - 1))
fips = df_clean['FIPS'].tolist()
values = df_clean['percent_no_internet'].tolist()
fig = ff.create_choropleth(
fips=fips, values=values,
binning_endpoints=endpts,
colorscale=colorscale,
show_state_data=False,
show_hover=True, centroid_marker={'opacity': 0},
asp=2.9, title='Percentage of People with No Internet',
legend_title='% without internet'
)
iplot(fig, filename='choropleth_full_usa')
Overall, this dataset is incomplete at the moment, but may be expanded upon with the 2018 update of the data. This was a valuable exploration, as we can possbily use the data here to train a model and generate some predictive data. As I learn more about Machine Learning, this seems very feasible to project the possible percentage of households without internet based upon median income, education level, and rate of poverty in the county. Since there are three factors for comparison, it stands to reason that the data produced would be accurate.
It is logical but supported by data that areas with a higher median income, lower poverty rate, and higher education level tend to have fewer internet deserts. This can help focus on areas that may have particularly good internet solutions, or areas that need it most, but were not able to be included here. I would encourage focus on the areas excluded from the data due to statistical insignificance (i.e. most of the midwest). This could be an area of further analysis with census data and public policy, especially in light of recent net neutrality decisions. In this US News article, there are many proposed solutions to promote equity for the most vulnerable members of the households discussed here.
Internet access, and data protection may become some of the most important areas of public policy, and there are many viable solutions here such as providing students with wireless hotspots or inexpensive chromebooks with which they can do homework. There are also solutions in place currently, like a low-cost internet package from Comcast for $10 per month for qualifying low-income customers. However, this may not be a widely known option. Solutions such as these may start to correct some of the sources of the internet inequity such as income and education.
Alternative solutions might include municipal broadband for instance, Chattanooga, Tennessee is widely cited as a forerunner in the municipal broadband movement. It offers low-cost, high-speed internet service that is well-rated by outside agencies as shown here in this Tech Crunch article. This model might be of consideration to many areas that are experiencing low rates of access, as it also will create jobs and improve access for children and adults to things like education, work and more. Longitudinal research on places like Chattanooga will be crucial to determine viability over time given cost, maintenance and satisfaction.